Predator-mediated coexistence: an equilibrium interpretation

I have constructed a spatially distributed analytical model of predators, superior prey competitors, and inferior prey competitors, based on the limiting deterministic version of a simulation model by Caswell (1978). Persistence regions for the three populations are mapped in parameter space. Conceptually shrinking the system from infinite size (i.e. infinitely many spatial “cells”) to some finite size introduces demographic stochasticity, increasing the chance of extinction of one or more populations within a given time interval. But some of the finite (stochastic) system's behavior, such as any tendency to damp perturbations, can be related to the behavior of the deterministic system at the same location in parameter space.     

Identification of small scale biochemical networks based on general type system perturbations

New technologies enable acquisition of large data-sets containing genomic, proteomic and metabolic information that describe the state of a cell. These data-sets call for systematic methods enabling relevant information about the inner workings of the cell to be extracted. One important issue at hand is the understanding of the functional interactions between genes, proteins and metabolites. We here present a method for identifying the dynamic interactions between biochemical components within the cell, in the vicinity of a steady-state. Key features of the proposed method are that it can deal with data obtained under perturbations of any system parameter, not only concentrations of specific components, and that the direct effect of the perturbations does not need to be known. This is important as concentration perturbations are often difficult to perform in biochemical systems and the specific effects of general type perturbations are usually highly uncertain, or unknown. The basis of the method is a linear least-squares estimation, using time-series measurements of concentrations and expression profiles, in which system states and parameter perturbations are estimated simultaneously. An important side-effect of also employing estimation of the parameter perturbations is that knowledge of the system's steady-state concentrations, or activities, is not required and that deviations from steady-state prior to the perturbation can be dealt with. Time derivatives are computed using a zero-order hold discretization, shown to yield significant improvements over the widely used Euler approximation. We also show how network interactions with dynamics that are too fast to be captured within the available sampling time can be determined and excluded from the network identification. Known and unknown moiety conservation relationships can be processed in the same manner. The method requires that the number of samples equals at least the number of network components and, hence, is at present restricted to relatively small-scale networks. We demonstrate herein the performance of the method on two small-scale in silico genetic networks.     

‘Glocal’ Robustness Analysis and Model Discrimination for Circadian Oscillators

To characterize the behavior and robustness of cellular circuits with many unknown parameters is a major challenge for systems biology. Its difficulty rises exponentially with the number of circuit components. We here propose a novel analysis method to meet this challenge. Our method identifies the region of a high-dimensional parameter space where a circuit displays an experimentally observed behavior. It does so via a Monte Carlo approach guided by principal component analysis, in order to allow efficient sampling of this space. This ‘global’ analysis is then supplemented by a ‘local’ analysis, in which circuit robustness is determined for each of the thousands of parameter sets sampled in the global analysis. We apply this method to two prominent, recent models of the cyanobacterial circadian oscillator, an autocatalytic model, and a model centered on consecutive phosphorylation at two sites of the KaiC protein, a key circadian regulator. For these models, we find that the two-sites architecture is much more robust than the autocatalytic one, both globally and locally, based on five different quantifiers of robustness, including robustness to parameter perturbations and to molecular noise. Our ‘glocal’ combination of global and local analyses can also identify key causes of high or low robustness. In doing so, our approach helps to unravel the architectural origin of robust circuit behavior. Complementarily, identifying fragile aspects of system behavior can aid in designing perturbation experiments that may discriminate between competing mechanisms and different parameter sets.     

Mechanisms for stabilisation and destabilisation of systems of reaction-diffusion equations

Potential mechanisms for stabilising and destabilising the spatially uniform steady states of systems of reaction-diffusion equations are examined. In the first instance the effect of introducing small periodic perturbations of the diffusion coefficients in a general system of reaction-diffusion equations is studied. Analytical results are proved for the case where the uniform steady state is marginally stable and demonstrate that the effect on the original system of such perturbations is one of stabilisation. Numerical simulations carried out on an ecological model of Levin and Segel (1976) confirm the analysis as well as extending it to the case where the perturbations are no longer small. Spatio-temporal delay is then introduced into the model. Analytical and numerical results are presented which show that the effect of the delay is to destabilise the original system.     

Solitary wave and spatially locked solitary pattern in a chemical reaction system

We investigate the behavior of a one-dimensional two component dynamical system. The dynamical equations are obtained by extracting an essence out of equations which describe the behavior of a biochemical reaction catalyzed by an allosteric protein. The obtained dynamical equations are similar to van der Pol equations. The dynamical equations are solved numerically. In the continuous system, a solitary wave is found to occur in certain ranges of the parameter space. The condition of occurrence of the solitary wave is investigated. The solitary wave can be induced by various initial perturbations, including rectangular ones with space-wise length longer than a certain critical value. The property of the solitary wave is similar to that of the impulses in nervous systems. In the discrete system, a spatially locked solitary pattern is found to occur in certain ranges of the parameter space.     

Global robust power-rate stability of delayed genetic regulatory networks with noise perturbations

In this paper, by using the Lyapunov method, Itô’s differential formula and linear matrix inequality (LMI) approach, the global robust power-rate stability in mean square is discussed for genetic regulatory networks with unbounded time-varying delay, noise perturbations and parameter uncertainties. Sufficient conditions are given to ensure the robust power-rate stability (in mean square) of the genetic regulatory networks. Meanwhile, the criteria ensuring global power-rate stability in mean square are a byproduct of the criteria guaranteeing global robust power-rate stability in mean square. The obtained conditions are derived in terms of linear matrix inequalities (LMIs) which are easy to be verified via the LMI toolbox. An illustrative example is given to show the effectiveness of the obtained result.     

Reducing the allowable kinetic space by constructing ensemble of dynamic models with the same steady-state flux

Dynamic models of metabolism are instrumental for gaining insight and predicting possible outcomes of perturbations. Current approaches start from the selection of lumped enzyme kinetics and determine the parameters within a large parametric space. However, kinetic parameters are often unknown and obtaining these parameters requires detailed characterization of enzyme kinetics. In many cases, only steady-state fluxes are measured or estimated, but these data have not been utilized to construct dynamic models. Here, we extend the previously developed Ensemble Modeling methodology by allowing various kinetic rate expressions and employing a more efficient solution method for steady states. We show that anchoring the dynamic models to the same flux reduces the allowable parameter space significantly such that sampling of high dimensional kinetic parameters becomes meaningful. The methodology enables examination of the properties of the model's structure, including multiple steady states. Screening of models based on limited steady-state fluxes or metabolite profiles reduces the parameter space further and the remaining models become increasingly predictive. We use both succinate overproduction and central carbon metabolism in Escherichia coli as examples to demonstrate these results.     

Synaptic efficacy shapes resource limitations in working memory

Working memory (WM) is limited in its temporal length and capacity. Classic conceptions of WM capacity assume the system possesses a finite number of slots, but recent evidence suggests WM may be a continuous resource. Resource models typically assume there is no hard upper bound on the number of items that can be stored, but WM fidelity decreases with the number of items. We analyze a neural field model of multi-item WM that associates each item with the location of a bump in a finite spatial domain, considering items that span a one-dimensional continuous feature space. Our analysis relates the neural architecture of the network to accumulated errors and capacity limitations arising during the delay period of a multi-item WM task. Networks with stronger synapses support wider bumps that interact more, whereas networks with weaker synapses support narrower bumps that are more susceptible to noise perturbations. There is an optimal synaptic strength that both limits bump interaction events and the effects of noise perturbations. This optimum shifts to weaker synapses as the number of items stored in the network is increased. Our model not only provides a circuit-based explanation for WM capacity, but also speaks to how capacity relates to the arrangement of stored items in a feature space.     

Dynamics and bifurcation of a model for hormonal control of the menstrual cycle with inhibin delay

A system of 13 ordinary differential equations with 42 parameters is presented to model hormonal regulation of the menstrual cycle. For an excellent fit to clinical data, the model requires a 36 h time delay for the effect of inhibin on the synthesis of follicle stimulating hormone. Biological and mathematical reasons for this delay are discussed. Bifurcations with respect to changes in three important parameters are examined. One parameter represents the level of estradiol adequate for significant synthesis of luteinizing hormone. Bifurcation diagrams with respect to this parameter reveal an interval of parameter values for which a unique stable periodic solution exists and this solution represents a menstrual cycle during which ovulation occurs. The second parameter measures mass transfer between the first two stages of ovarian development and is indicative of healthy follicular growth. The third parameter is the time delay. Changes in the second parameter and the time delay affect the size of the uniqueness interval defined with respect to the first parameter. Saddle-node, transcritical and degenerate Hopf bifurcations are studied.     

The stability of entrainment conditions forRLC coupled van der pol oscillators used as a model for intestinal electrical rhythms

A commonly accepted mathematical model for the slow wave electrical activity of the gastro-intestinal tract of humans and animals comprises a set of interconnected relaxation oscillators. The method of harmonic balance is used here to obtain analytical results for the entrained frequencies and amplitudes of two oscillators coupled with a parallelRLC network. By perturbations and linearisation about these values the conditions for stable limit-cycles are found and regions in theRLC parameter space which give one or two stable limit-cycle conditions are derived. These analytical results are compared with simulated results and found to creelate well for a waveshape factor of ε=0.1 and fairly well for ε=1.0. The single limit-cycle region corresponds to the requirement for a single mode having a frequency higher than the uncoupled value in small-intestinal data, while the double limit-cycle region corresponds to the two rhythms found in human large-intestinal activity.     

Excitable Population Dynamics,Biological Control Failure,and Spatiotemporal Pattern Formation in a Model Ecosystem

Biological control has been attracting an increasing attention over the last two decades as an environmentally friendly alternative to the more traditional chemical-based control. In this paper, we address robustness of the biological control strategy with respect to fluctuations in the controlling species density. Specifically, we consider a pest being kept under control by its predator. The predator response is assumed to be of Holling type III, which makes the system’s kinetics “excitable.” The system is studied by means of mathematical modeling and extensive numerical simulations. We show that the system response to perturbations in the predator density can be completely different in spatial and non-spatial systems. In the nonspatial system, an overcritical perturbation of the population density results in a pest outbreak that will eventually decay with time, which can be regarded as a success of the biological control strategy. However, in the spatial system, a similar perturbation can drive the system into a self-sustained regime of spatiotemporal pattern formation with a high pest density, which is clearly a biological control failure. We then identify the parameter range where the biological control can still be successful and describe the corresponding regime of the system dynamics. Finally, we identify the main scenarios of the system response to the population density perturbations and reveal the corresponding structure of the parameter space of the system. A. Morozov is on leave from Shirshov Institute of Oceanology, Russian Academy of Science, Nakhimovsky Prosp. 36, Moscow 117218, Russia.     

Overcompensatory recruitment and generation delay in discrete age-structured population models

 The effect of overcompensatory recruitment and the combined effect of overcompensatory recruitment and generation delay in discrete nonlinear age-structured population models is studied. Considering overcompensatory recruitment alone, we present formal proofs of the supercritical nature of bifurcations (both flip and Hopf) as well as an extensive analysis of dynamics in unstable parameter regions. One important finding here is that in case of small and moderate year to year survival probabilities there are large regions in parameter space where the qualitative behaviour found in a general n+1 dimensional model is retained already in a one-dimensional model. Another result is that the dynamics at or near the boundary of parameter space may be very complicated. Generally, generation delay is found to act as a destabilizing effect but its effect on dynamics is by no means unique. The most profound effect occurs in the n-generation delay cases. In these cases there is no stable equilibrium X ^* at all, but whenever X ^* small, a stable cycle of period n+1 where the periodic points in the cycle are on a very special form. In other cases generation delay does not alter the dynamics in any substantial way. Received 25 April 1995; received in revised form 21 November 1995     

A Method for Estimating the Predictive Power in a Model of a Biological System with Low Sensitivity to Parameters

The ambiguity of parameter estimates for the model of a biological system may be due to low sensitivity of the model to perturbations of input data (parameters), which mathematically reflects biological mechanisms of robustness. We developed a novel method for estimating the predictive power of a model with the ambiguity of parameter estimates. The predictions are understood as a correct reproduction of the system behavior by the model when changing input data and parameters. The method is based on the relative sensitivity analysis of the fitted model to stiff parameters of the predicted model. The application principles of our approach are demonstrated using a model for the formation of the mRNA expression pattern of the hb gene in the Drosophila embryo and its ability to predict the hb pattern in the Kr null mutant. The nonlinear nature of the system is simulated by a saturating sigmoid function, which is the cause of low sensitivity. Our method allows us to estimate the predictive power of the model and uncover the causes of poor predictions, as well as choose the relevant level of the model detail in terms of predictions.     

Long-wavelength instability of periodic flows and helicon waves in electron magnetohydrodynamics

The stability of periodic flows and helicon waves against large-scale perturbations is investigated analytically in resistive electron magnetohydrodynamics by the method of two-scale expansions. It is shown that long-wavelength perturbations of a Kolmogorov-type flow are destabilized by the effect of negative resistivity. The destabilization of long-wavelength perturbations of a Beltrami-type helical flow and helicon waves is related to the microhelicity of the primary flow (wave). The instability of long-wavelength perturbations of an anisotropic helical flow is found to result from both the effect of negative resistivity and the effect associated with the microhelical nature of the flow. The criteria for the onset of the corresponding instabilities are derived. Numerical simulations are carried out based on nonlinear electron magnetohydrodynamic equations with initial conditions corresponding to the analytic formulation of the problem. The results of simulations on the whole confirm analytical results in the parameter range in which the latter are applicable and, in addition, extend the stability analysis to the parameter ranges that are beyond the scope of analytic approximations.     

Dynamics of chemostat culture:the effect of a delay in cell response

The effect of introducing a delay between a change in concentration of limiting nutrient and the response in division rate of the population is investigated. It is shown that this might or might not introduce a delay in the control of the chemostat system, depending upon how the uptake rate is assumed to double during the cell cycle. An area in the parameter space where autonomous oscillations will occur for one specific model is found analytically. This result is confirmed by computer simulations.     

Weak resonant double Hopf bifurcations in an inertial four-neuron model with time delay

In this paper, a four-neuron delayed bidirectional associative memory (BAM) model with inertia is considered. Weak resonant double Hopf bifurcations are completely analyzed in the parameter space of the coupling weight and the coupling delay by the perturbation-incremental scheme (PIS). Numerical simulations are given for justifying the theoretical results. To the best of our knowledge, the paper is the first one to introduce inertia to a four-neuron delayed system and clarify the relationship between system parameters and dynamical behaviors.     

Modelling and analysis of time-lags in some basic patterns of cell proliferation

 In this paper, we present a systematic approach for obtaining qualitatively and quantitatively correct mathematical models of some biological phenomena with time-lags. Features of our approach are the development of a hierarchy of related models and the estimation of parameter values, along with their non-linear biases and standard deviations, for sets of experimental data. We demonstrate our method of solving parameter estimation problems for neutral delay differential equations by analyzing some models of cell growth that incorporate a time-lag in the cell division phase. We show that these models are more consistent with certain reported data than the classic exponential growth model. Although the exponential growth model provides estimates of some of the growth characteristics, such as the population-doubling time, the time-lag growth models can additionally provide estimates of: (i) the fraction of cells that are dividing, (ii) the rate of commitment of cells to cell division, (iii) the initial distribution of cells in the cell cycle, and (iv) the degree of synchronization of cells in the (initial) cell population. Received: 15 September 1997/Revised version: 1 April 1998     

Slow Passage Through a Hopf Bifurcation in Excitable Nerve Cables: Spatial Delays and Spatial Memory Effects

It is well established that in problems featuring slow passage through a Hopf bifurcation (dynamic Hopf bifurcation) the transition to large-amplitude oscillations may not occur until the slowly changing parameter considerably exceeds the value predicted from the static Hopf bifurcation analysis (temporal delay effect), with the length of the delay depending upon the initial value of the slowly changing parameter (temporal memory effect). In this paper we introduce new delay and memory effect phenomena using both analytic (WKB method) and numerical methods. We present a reaction–diffusion system for which slowly ramping a stimulus parameter (injected current) through a Hopf bifurcation elicits large-amplitude oscillations confined to a location a significant distance from the injection site (spatial delay effect). Furthermore, if the initial current value changes, this location may change (spatial memory effect). Our reaction–diffusion system is Baer and Rinzel’s continuum model of a spiny dendritic cable; this system consists of a passive dendritic cable weakly coupled to excitable dendritic spines. We compare results for this system with those for nerve cable models in which there is stronger coupling between the reactive and diffusive portions of the system. Finally, we show mathematically that Hodgkin and Huxley were correct in their assertion that for a sufficiently slow current ramp and a sufficiently large cable length, no value of injected current would cause their model of an excitable cable to fire; we call this phenomenon “complete accommodation.”     

Evaluating the Use of Global Sensitivity Analysis in Dynamic MFA

Dynamic material flow analysis (MFA) provides information about material usage over time and consequent changes in material stocks and flows. In order to understand the effect of limited data quality and model assumptions on MFA results, the use of sensitivity analysis methods in dynamic MFA studies has been on the increase. So far, sensitivity analysis in dynamic MFA has been conducted by means of a one‐at‐a‐time method, which tests parameter perturbations individually and observes the outcomes on output. In contrast to that, variance‐based global sensitivity analysis decomposes the variance of the model output into fractions caused by the uncertainty or variability of input parameters. The present study investigates interaction and time‐delay effects of uncertain parameters on the output of an archetypal input‐driven dynamic material flow model using variance‐based global sensitivity analysis. The results show that determining the main (first‐order) effects of parameter variations is often sufficient in dynamic MFA because substantial effects attributed to the simultaneous variation of several parameters (higher‐order effects) do not appear for classical setups of dynamic material flow models. For models with time‐varying parameters, time‐delay effects of parameter variation on model outputs need to be considered, potentially boosting the computational cost of global sensitivity analysis. Finally, the implications of exploring the sensitivities of model outputs with respect to parameter variations in the archetypical model are used to derive model‐ and goal‐specific recommendations on choosing appropriate sensitivity analysis methods in dynamic MFA.     

The contribution of muscle properties in the control of explosive movements

Explosive movements such as throwing, kicking, and jumping are characterized by high velocity and short movement time. Due to the fact that latencies of neural feedback loops are long in comparison to movement times, correction of deviations cannot be achieved on the basis of neural feedback. In other words, the control signals must be largely preprogrammed. Furthermore, in many explosive movements the skeletal system is mechanically analogous to an inverted pendulum; in such a system, disturbances tend to be amplified as time proceeds. It is difficult to understand how an inverted-pendulum-like system can be controlled on the basis of some form of open loop control (albeit during a finite period of time only). To investigate if actuator properties, specifically the force-length-velocity relationship of muscle, reduce the control problem associated with explosive movement tasks such as human vertical jumping, a direct dynamics modeling and simulation approach was adopted. In order to identify the role of muscle properties, two types of open loop control signals were applied: STIM(t), representing the stimulation of muscles, and MOM(t), representing net joint moments. In case of STIM control, muscle properties influence the joint moments exerted on the skeleton; in case of MOM control, these moments are directly prescribed. By applying perturbations and comparing the deviations from a reference movement for both types of control, the reduction of the effect of disturbances due to muscle properties was calculated. It was found that the system is very sensitive to perturbations in case of MOM control; the sensitivity to perturbations is markedly less in case of STIM control. It was concluded that muscle properties constitute a peripheral feedback system that has the advantage of zero time delay. This feedback system reduces the effect of perturbations during human vertical jumping to such a degree that when perturbations are not too large, the task may be performed successfully without any adaptation of the muscle stimulation pattern.