Control of interaction strength in a network of the true slime mold by a microfabricated structure

The plasmodium of the true slime mold, Physarum polycephalum, which shows various nonlinear oscillatory phenomena, for example, in its thickness, protoplasmic streaming and concentration of intracellular chemicals, can be regarded as a collective of nonlinear oscillators. The plasmodial oscillators are interconnected by microscale tubes whose dimensions can be closely related to the strength of interaction between the oscillators. Investigation of the collective behavior of the oscillators under the conditions in which the interaction strength can be systematically controlled gives significant information on the characteristics of the system. In this study, we proposed a living model system of a coupled oscillator system in the Physarum plasmodium. We patterned the geometry and dimensions of the microscale tube structure in the plasmodium by a microfabricated structure (microstructure). As the first step, we constructed a two-oscillator system for the plasmodium that has two wells (oscillator part) and a channel (coupling part). We investigated the oscillation behavior by monitoring the thickness oscillation of the plasmodium in the microstructure with various channel widths. It was found that the oscillation behavior of two oscillators dynamically changed depending on the channel width. Based on the results of measurements of the tube dimensions and the velocity of the protoplasmic streaming in the tube, we discuss how the channel width relates to the interaction strength of the coupled oscillator system.     

Stochastic noise interferes coherently with a model biological clock and produces specific dynamic behaviour.

The influence of noise is unavoidable in all living systems. Its impact on a model of a biological clock, normally running in regular oscillating modes, is examined. It is shown that in a specific system in which endogenous rhythmicity is produced by a beat oscillator acting on a feedback coupled metabolic pool system, noise can act coherently to produce unexpected dynamic behaviour, running from regular over pseudo-regular to irregular time-structures. If the biological system consists of a set of identical weakly coupled cells, stochasticity may lead to phase decoupling producing irregular spatio-temporal patterns. Synchronization via phase resetting can be achieved by external short-time temperature pulses. Explicit results are obtained for the well-studied circadian photosynthesis oscillations in plants performing crassulacean acid metabolism. Because of the generic structure of the underlying nonlinear dynamics they can, however, be regarded as a general property of the influence of noise on nonlinear excitable systems with fixed points occuring close to limit cycles.     

Fluorescence correlation spectroscopy: past, present, future

In recent years fluorescence correlation spectroscopy (FCS) has become a routine method for determining diffusion coefficients, chemical rate constants, molecular concentrations, fluorescence brightness, triplet state lifetimes, and other molecular parameters. FCS measures the spatial and temporal correlation of individual molecules with themselves and so provides a bridge between classical ensemble and contemporary single-molecule measurements. It also provides information on concentration and molecular number fluctuations for nonlinear reaction systems that complement single-molecule measurements. Typically implemented on a fluorescence microscope, FCS samples femtoliter volumes and so is especially useful for characterizing small dynamic systems such as biological cells. In addition to its practical utility, however, FCS provides a window on mesoscopic systems in which fluctuations from steady states not only provide the basis for the measurement but also can have important consequences for the behavior and evolution of the system. For example, a new and potentially interesting field for FCS studies could be the study of nonequilibrium steady states, especially in living cells.     

Chaos and evolution

There is growing interest in applying nonlinear methods to evolutionary biology. With good reason: the living world is full of nonlinearities, responsible for steady states, regular oscillations, and chaos in biological systems. Evolutionists may find nonlinear dynamics important in studying short-term dynamics of changes in genotype frequency, and in understanding selection and its constraints. More speculatively, dynamical systems theory may be important because nonlinear fluctuations in some traits may sometimes be favored by selection, and because some long-run patterns of evolutionary change could be described using these methods.     

Link between truncated fractals and coupled oscillators in biological systems

This article aims at providing a new theoretical insight into the fundamental question of the origin of truncated fractals in biological systems. It is well known that fractal geometry is one of the characteristics of living organisms. However, contrary to mathematical fractals which are self-similar at all scales, the biological fractals are truncated, i.e. their self-similarity extends at most over a few orders of magnitude of separation. We show that nonlinear coupled oscillators, modeling one of the basic features of biological systems, may generate truncated fractals: a truncated fractal pattern for basin boundaries appears in a simple mathematical model of two coupled nonlinear oscillators with weak dissipation. This fractal pattern can be considered as a particular hidden fractal property. At the level of sufficiently fine precision technique the truncated fractality acts as a simple structure, leading to predictability, but at a lower level of precision it is effectively fractal, limiting the predictability of the long-term behavior of biological systems. We point out to the generic nature of our result.     

Extended Kalman Filter for Estimation of Parameters in Nonlinear State-Space Models of Biochemical Networks

It is system dynamics that determines the function of cells, tissues and organisms. To develop mathematical models and estimate their parameters are an essential issue for studying dynamic behaviors of biological systems which include metabolic networks, genetic regulatory networks and signal transduction pathways, under perturbation of external stimuli. In general, biological dynamic systems are partially observed. Therefore, a natural way to model dynamic biological systems is to employ nonlinear state-space equations. Although statistical methods for parameter estimation of linear models in biological dynamic systems have been developed intensively in the recent years, the estimation of both states and parameters of nonlinear dynamic systems remains a challenging task. In this report, we apply extended Kalman Filter (EKF) to the estimation of both states and parameters of nonlinear state-space models. To evaluate the performance of the EKF for parameter estimation, we apply the EKF to a simulation dataset and two real datasets: JAK-STAT signal transduction pathway and Ras/Raf/MEK/ERK signaling transduction pathways datasets. The preliminary results show that EKF can accurately estimate the parameters and predict states in nonlinear state-space equations for modeling dynamic biochemical networks.     

Application of the mathematics of coupled oscillator systems to the analysis of the neural control of locomotion.

In many animals, the activities of limb motor neurons are rhythmic during locomotion. In some animals it is known that each limb is innervated by a local control center that resides in a discrete portion of the central nervous system. Each local control center is a biological oscillator. Since each limb moves with the same frequency as each other limb and with regulated phase delay with respect to each other limb, then it follows that the local control centers are coupled to one another. The locomotory pattern generator within the central nervous system is therefore a coupled oscillator system. The mathematics of coupled oscillator systems can assist in the construction of a model of the neural pattern generator. This model can be utilized to formulate testable predictions concerning the neural control of locomotion. Experimental data gathered from organisms in several phylums are consistent with the predictions of the model.     

The Teorell membrane oscillator as a mechano-electric transducer

Summary The results of a recent quantitative analysis of the Teorell membrane oscillator are utilized to explore its role as an excitability analogue. Special attention is paid to its role as a mechano-electric transducer. A membrane of exceptionally well-defined pore structure has been used in this study. The analogue properties arise from nonlinear coupling between water and salt fluxes. When the membrane is simultaneously subjected to controlled gradients of hydrostatic pressure, electrical potential and concentration, bi-stable stationary states can be produced. These arise from the opposing effects of pressure and electro-osmosis on the volume flow. Transitions between these states show hysteresis. The factors governing such transitions are analogous to certain types of stimuli encountered in the natural excitation process. The membrane system also shows oscillatory behavior when the hydrostatic pressure gradient is allowed to vary under constant current conditions. This property is related to the bi-stable stationary state phenomena and is compared to the regenerative behavior found in biologically excitable tissues. Particular emphasis is placed upon analogies between the membrane oscillator and certain natural tissues. The importance of the nonlinear nature of the force-flux coupling in the analogue is stressed, and its possible relevance to biological excitability indicated. Some consideration is also given to the role of electro-osmotic flux coupling in biological tissues.     

Controlling the geometry and the coupling strength of the oscillator system in plasmodium ofPhysarum polycephalum by microfabricated structure

Summary The plasmodium of the true slime moldPhysarum polycephalum, which shows various oscillatory phenomena, can be regarded as a collective of nonlinear oscillators. Partial bodies in the plasmodium, which are assumed to be nonlinear oscillators, are mutually connected by microscale tubes named plasmodial strand. The interactions among the oscillators can be strongly affected by the geometry and the dimension of the tube network. Investigation of the collective behavior under the condition that the configuration of the network can be manipulated gives significant information on the characteristics of the plasmodium from the viewpoint of nonlinear dynamics. In this study, we have developed a new method to control the geometry and the tube dimension of the plasmodium with a microfabricated structure. It is shown that the geometry of the plasmodium can be manipulated with a microstructure which is fabricated of ultrathick photoresist resin by photolithographic processes. In order to confirm that not only the geometry but also the dimension of the tubes can be controlled with the microstructure, we observed the cross section of the patterned plasmodium with a three-dimensional internal-structure microscope. By observing the oscillatory behavior of the partial bodies of the patterned plasmodium, it was confirmed that the coupling strength between two oscillators, which corresponds to the dimension of the plasmodial strand, can be controlled by the microstructure. It is concluded that the present method is suitable for further studies of the network of Physarum plasmodium as a collective nonlinear oscillator system.     

Multiple pulse interactions and averaging in systems of coupled neural oscillators

Oscillators coupled strongly are capable of complicated behavior which may be pathological for biological control systems. Nevertheless, strong coupling may be needed to prevent asynchrony. We discuss how some neural networks may be designed to achieve only simple locking behavior when the coupling is strong. The design is based on the fact that the method of averaging produces equations that are capable only of locking or drift, not pathological complexity. Furthermore, it is shown that oscillators that interact by means of multiple pulses per cycle, dispersed around the cycle, behave like averaged equations, even if the number of pulses is small. We discuss the biological intuition behind this scheme, and show numerically that it works when the oscillators are taken to be composites, each unit of which is governed by a well-known model of a neural oscillator. Finally, we describe numerical methods for computing from equations for coupled limit cycle oscillators the averaged coupling functions of our theory.Research partially supported by the National Science Foundation under grants DMS 8796235 and DMS 8701405 and the Air Force Office of Scientific Research under University Research Contract F 49620-C-0131 to Northeastern University     

Roles of protein ubiquitination and degradation kinetics in biological oscillations

Protein ubiquitination and degradation play important roles in many biological functions and are associated with many human diseases. It is well known that for biochemical oscillations to occur, proper degradation rates of the participating proteins are needed. In most mathematical models of biochemical reactions, linear degradation kinetics has been used. However, the degradation kinetics in real systems may be nonlinear, and how nonlinear degradation kinetics affects biological oscillations are not well understood. In this study, we first develop a biochemical reaction model of protein ubiquitination and degradation and calculate the degradation rate against the concentration of the free substrate. We show that the protein degradation kinetics mainly follows the Michaelis-Menten formulation with a time delay caused by ubiquitination and deubiquitination. We then study analytically how the Michaelis-Menten degradation kinetics affects the instabilities that lead to oscillations using three generic oscillation models: 1) a positive feedback mediated oscillator; 2) a positive-plus-negative feedback mediated oscillator; and 3) a negative feedback mediated oscillator. In all three cases, nonlinear degradation kinetics promotes oscillations, especially for the negative feedback mediated oscillator, resulting in much larger oscillation amplitudes and slower frequencies than those observed with linear kinetics. However, the time delay due to protein ubiquitination and deubiquitination generally suppresses oscillations, reducing the amplitude and increasing the frequency of the oscillations. These theoretical analyses provide mechanistic insights into the effects of specific proteins in the ubiquitination-proteasome system on biological oscillations.     

Advances in the analysis of isothermal titration calorimetry data for ligand-DNA interactions

Isothermal titration calorimetry (ITC) is a well established technique for the study of biological interactions. The strength of ITC is that it directly measures enthalpy changes associated with interactions. Experiments can also yield binding isotherms allowing quantification of equilibrium binding constants, hence an almost complete thermodynamic profile can be established. Principles and application of ITC have been well documented over recent years, experimentally the technique is simple to use and in ideal scenarios data analysis is trivial. However, ITC experiments can be designed such that previously inaccessible parameters can be evaluated. We outline some of these advances, including (1) exploiting different experimental conditions; (2) low affinity systems; (3) high affinity systems and displacement assays. In addition we ask the question: What if data cannot be fit using the fitting functions incorporated in the data-analysis software that came with your ITC? Examples where such data might be generated include systems following non 1:n binding patterns and systems where binding is coupled to other events such as ligand dissociation. Models dealing with such data are now appearing in literature and we summarise examples relevant for the study of ligand-DNA interactions.     

Interaction of coupled nonlinear oscillators having different intrinsic resting levels

The interaction among coupled oscillators is governed by oscillator properties (intrinsic frequency and amplitude) and coupling mechanisms. This study considers another oscillator property, the intrinsic resting level, and evaluates its role in governing oscillator interactions. The results of computer experiments on a chain of either three or five bidirectionally coupled nonlinear oscillators, suggest that an intrinsic resting level gradient, if present, is one of the factors governing the interaction between coupled oscillators. If there is no intrinsic frequency gradient, then an intrinsic resting level gradient is sufficient to produce many features of interaction among coupled oscillators. If both intrinsic frequency and intrinsic resting level gradients are present, then both of them determine the manner in which the coupled oscillators interact with each other.     

Application of a new method of nonlinear dynamical system identification to biochemical problems

The system identification method for a variety of nonlinear dynamic models is elaborated. The problem of identification of an original nonlinear model presented as a system of ordinary differential equations in the Cauchy explicit form with a polynomial right part reduces to the solution of the system of linear equations for the constants of the dynamical model. In other words, to construct an integral model of the complex system (phenomenon), it is enough to collect some data array characterizing the time-course of dynamical parameters of the system. Collection of such a data array has always been a problem. However difficulties emerging are, as a rule, not principal and may be overcome almost without exception. The potentialities of the method under discussion are demonstrated by the example of the test problem of multiparametric nonlinear oscillator identification. The identification method proposed may be applied to the study of different biological systems and in particular the enzyme kinetics of complex biochemical reactions.     

A modified Ising model of Barabási–Albert network with gene-type spins

The central question of systems biology is to understand how individual components of a biological system such as genes or proteins cooperate in emerging phenotypes resulting in the evolution of diseases. As living cells are open systems in quasi-steady state type equilibrium in continuous exchange with their environment, computational techniques that have been successfully applied in statistical thermodynamics to describe phase transitions may provide new insights to the emerging behavior of biological systems. Here we systematically evaluate the translation of computational techniques from solid-state physics to network models that closely resemble biological networks and develop specific translational rules to tackle problems unique to living systems. We focus on logic models exhibiting only two states in each network node. Motivated by the apparent asymmetry between biological states where an entity exhibits boolean states i.e. is active or inactive, we present an adaptation of symmetric Ising model towards an asymmetric one fitting to living systems here referred to as the modified Ising model with gene-type spins. We analyze phase transitions by Monte Carlo simulations and propose a mean-field solution of a modified Ising model of a network type that closely resembles a real-world network, the Barabási–Albert model of scale-free networks. We show that asymmetric Ising models show similarities to symmetric Ising models with the external field and undergoes a discontinuous phase transition of the first-order and exhibits hysteresis. The simulation setup presented herein can be directly used for any biological network connectivity dataset and is also applicable for other networks that exhibit similar states of activity. The method proposed here is a general statistical method to deal with non-linear large scale models arising in the context of biological systems and is scalable to any network size.

     

Bio-soliton model that predicts non-thermal electromagnetic frequency bands,that either stabilize or destabilize living cells

Solitons, as self-reinforcing solitary waves, interact with complex biological phenomena such as cellular self-organization. A soliton model is able to describe a spectrum of electromagnetism modalities that can be applied to understand the physical principles of biological effects in living cells, as caused by endogenous and exogenous electromagnetic fields and is compatible with quantum coherence. A bio-soliton model is proposed, that enables to predict which eigen-frequencies of non-thermal electromagnetic waves are life-sustaining and which are, in contrast, detrimental for living cells. The particular effects are exerted by a range of electromagnetic wave eigen-frequencies of one-tenth of a Hertz till Peta Hertz that show a pattern of 12 bands, and can be positioned on an acoustic reference frequency scale. The model was substantiated by a meta-analysis of 240 published articles of biological electromagnetic experiments, in which a spectrum of non-thermal electromagnetic waves were exposed to living cells and intact organisms. These data support the concept of coherent quantized electromagnetic states in living organisms and the theories of Fröhlich, Davydov and Pang. It is envisioned that a rational control of shape by soliton-waves and related to a morphogenetic field and parametric resonance provides positional information and cues to regulate organism-wide systems properties like anatomy, control of reproduction and repair.     

A theoretical model of phase transitions in the human brain

An experiment using a multisensor SQUID (superconducting quantum interference device) array was performed by Kelso and colleagues (1992) which combined information from three different sources: perception, motor response, and brain signals. When an acoustic stimulus frequency is changed systematically, a spontaneous transition in coordination occurs at a critical frequency in both motor behavior and brain signals. Qualitatively analogous transitions are known for physical and biological systems such as changes in the coordination of human hand movements (Kelso 1981, 1984). In this paper we develop a theoretical model based on methods from the interdisciplinary field of synergetics (Haken 1983, 1987) and nonlinear oscillator theory that reproduces the main experimental features very well and suggests a formulation of a fundamental biophysical coupling. Received: 8 September 1993     

RF nonlinear interactions in living cells-I: nonequilibrium thermodynamic theory

Some biological experiments report effects that depend on low frequency modulation of a radiofrequency (RF) carrier. Such effects require nonlinear responses in biological preparations, which we show could be observed with great generality by the unique frequency signatures that would appear in the scattered RF energy. Following Illinger [Illinger (1982): Bioelectromagnetics 3:9-16], we considered a two part physical system. The greater part, dominated by the properties of water, interacts linearly with the RF field and is described by equilibrium thermodynamics. However, another, much smaller part, e.g., certain biological molecules and molecular subgroups, supports nonlinear interactions and is described by nonequilibrium thermodynamics. For example, a nonlinear interaction might result from scattering of RF photons from oscillators located in a region of strong field gradients, such as at membrane surfaces. A second nonlinear mechanism could appear if stress (elastic) waves were launched within the confines of the exposure vessel by RF heating. Amplitude modulation at angular frequency Omega of a carrier wave with angular frequency omega (omega < omega) produces two peaks in elastic stress in the cell structure during each period; that is, there is "full-wave demodulation." As a result of coherent nonlinear charge motion, modulation products could appear at frequencies omega +/- 2 omega and, in general, at omega +/- n 2 omega (n = 1, 2, em leader ) if vibrational harmonics at 2 n omega also are excited. Although in principle microwaves can alter the stability of a thermodynamic system by pumping a chemical transition, the degree of nonlinear coupling required for an observable instability is so great that its probability is effectively zero, unless field intensity is extremely high. A companion paper suggests an extremely sensitive method and the related instrumentation for detection of the spectrum scattered by living cells during exposure to amplitude modulated RF energy.     

Coherency and connectivity in oscillating neural networks: linear partialization analysis

 This paper studies the relation between the functional synaptic connections between two artificial neural networks and the correlation of their spiking activities. The model neurons had realistic non-oscillatory dynamic properties and the networks showed oscillatory behavior as a result of their internal synaptic connectivity. We found that both excitation and inhibition cause phase locking of the oscillating activities. When the two networks excite each other the oscillations synchronize with zero phase lag, whereas mutual inhibition between the networks resulted in an anti-phase (half period phase difference) synchronization. Correlations between the activities of the two networks can also be caused by correlated external inputs driving the systems (common input). Our analysis shows that when the networks exhibit oscillatory behavior and the rate of the common input is smaller than a characteristic network oscillator frequency, the cross-correlation functions between the activities of two systems still carry information about the mutual synaptic connectivity. This information can be retrieved with linear partialization, removing the influence of the common input. We further explored the network responses to periodic external input. We found that when the input is of a frequency smaller than a certain threshold, the network responds with bursts at the same frequency as the input. Above the threshold, the network responds with a fraction of the input frequency. This frequency threshold, characterizing the oscillatory properties of the network, is also found to determine the limit to which linear partialization works. Received: 20 October 1995 / Accepted in revised form: 20 May 1996