Interaction of maturation delay and nonlinear birth in population and epidemic models

 A population with birth rate function B(N) N and linear death rate for the adult stage is assumed to have a maturation delay T>0. Thus the growth equation N′(t)=B(N(t−T)) N(t−T) e⁻ d ₁ T−dN(t) governs the adult population, with the death rate in previous life stages d ₁≧0. Standard assumptions are made on B(N) so that a unique equilibrium N _e exists. When B(N) N is not monotone, the delay T can qualitatively change the dynamics. For some fixed values of the parameters with d ₁>0, as T increases the equilibrium N _e can switch from being stable to unstable (with numerically observed periodic solutions) and then back to stable. When disease that does not cause death is introduced into the population, a threshold parameter R ₀ is identified. When R ₀<1, the disease dies out; when R ₀>1, the disease remains endemic, either tending to an equilibrium value or oscillating about this value. Numerical simulations indicate that oscillations can also be induced by disease related death in a model with maturation delay. Received: 2 November 1998 / Revised version: 26 February 1999     

Population models for diseases with no recovery

An S I epidemic model with a general shape of density-dependent mortality and incidence rate is studied. The asymptotic behaviour is global convergence to an endemic equilibrium, above a threshold, and to a disease-free equilibrium, below the threshold. The effect of vaccination is then examined.     

Time-dependent fluorescence depolarization and Brownian rotational diffusion coefficients of macromolecules

Using a distribution function formalism, we have derived relations between the time-dependent fluorescence polarization anistropy r(t) = [I‖(t) ? I?(t)]/[I‖(t) + 2I?(t)] and the Brownian rotational diffusion coefficients of macromolecules. It is shown that in the most general case of a completely asymmetric body, five exponentials appear in r(t). Reduction to previously obtained simpler expressions are presented and discussed. Experiments were performed to measure r(t) for the complex of 1-anilino-8-naphthalene sulfonate (ANS) and apomyoglobin. The results revealed a spherical geometry, with a radius of 20.8 Å. Similar experiments were performed on the ANS–apohorse radish peroxidase complex. The results were inconclusive towards the geometry of the complex, but when assumed to be spherical, the radius was estimated to be 29.6 Å, consistent with the larger molecular weight of horse radish peroxidase.     

Lyapunov Functions and Global Stability for SIR and SIRS Epidemiological Models with Non-Linear Transmission

Lyapunov functions for two-dimension SIR and SIRS compartmental epidemic models with non-linear transmission rate of a very general form f(S,I) constrained by a few biologically feasible conditions are constructed. Global properties of these models including these with vertical and horizontal transmission, are thereby established. It is proved that, under the constant population size assumption, the concavity of the function f(S,I) with respect to the number of the infective hosts I ensures the uniqueness and the global stability of the positive endemic equilibrium state. AMS Classification 92D30 (primary), 34D20 (secondary)     

Analysis of a disease transmission model in a population with varying size

An S I R S epidemiological model with vital dynamics in a population of varying size is discussed. A complete global analysis is given which uses a new result to establish the nonexistence of periodic solutions. Results are discussed in terms of three explicit threshold parameters which respectively govern the increase of the total population, the existence and stability of an endemic proportion equilibrium and the growth of the infective population. These lead to two distinct concepts of disease eradication which involve the total number of infectives and their proportion in the population.Partially supported by NSF Grant No. DMS-8703631. This work was done while this author was visiting the University of VictoriaResearch supported in part by NSERC A-8965     

Natural selection and fitness entropy in a density-regulated population

The entropy H(p_o,p*) of a population with the initial allele frequency p_o given the equilibrium polymorphic frequency p* has been proposed as a measure of natural selection. In the present paper, we have extended this concept to include a particular aspect of density-dependent selection. We compared size trajectory of a population initially at genetic equilibrium, N(t), with the size trajectories of populations not initially at p*,N(t), but which do eventually converge to a common equilibrium allele frequency and equilibrium density, N*. The following experimentally testable hyopthesis was established. The total area defined by the difference between the trajectories of N(t) and N(t) as they converge to N* is directly proportional to the fitness entropy when population size is transformed using the density-dependent fitness value. Two properties of this relationship were noted. First, it is independent of the magnitude of natural selection and, secondly, it does not depend upon the initial population density as long as the equilibrium and nonequilibrium populations have the same initial numbers. This hypothesis was evaluated with experimental data on the flour beetle Tribolium castaneum.     

Photosynthesis in dynamic light: systems biology of unconventional chlorophyll fluorescence transients in Synechocystis sp. PCC 6803

Photosynthetic organisms live in a dynamic environment where light typically fluctuates around a mean level that is slowly drifting during the solar day. We show that the far-from-equilibrium photosynthesis occurring in a rapidly fluctuating light differs vastly from the stationary-flux photosynthesis attained in a constant or slowly drifting light. Photosynthetic organisms in a static or slowly drifting light can be characterized by a steady-state quantum yield of chlorophyll fluorescence emission F′ that is changing linearly with small and slow variations of the incident irradiance I+ΔI(t): F′(I+ΔI(t))≈ F_mean^′(dF)/(dI)·ΔI(t). In Synechocystis sp. PCC 6803, the linear approximation holds for an extended interval covering largely the static irradiance range experienced by the cyanobacteria in nature. The photosynthetic dynamism and, consequently, the dynamism of the chlorophyll fluorescence emission change dramatically when exposing the organism to a fluctuating irradiance. Harmonically-modulated irradiance I+ΔI · sin(2πt/T), T ≈ 1–25 s induces perpetual, far-from-equilibrium forced oscillations that are strongly non-linear, exhibiting significant hysteresis with multiple fluorescence levels corresponding to a single instantaneous level of the incident irradiance. We propose that, in nature, the far-from-equilibrium dynamic phenomena represent a significant correction to the steady-state photosynthetic activity that is typically investigated in laboratory. Analysis of the forced oscillations by the tools of systems biology suggests that the dynamism of photosynthesis observed in fluctuating light can be explained by a delayed action of regulatory agents.     

The convergence in distribution of some simple epidemics

A group of n susceptible individuals exposed to a contagious disease isconsidered. It is assumed that at each point in time one or more susceptible individuals can contract the disease. The progress of this simple batch epidemic is modeled by a stochastic process X_n(t), t[0, ∞), representing the number of infectiveindividuals at time t. In this paper our analysis is restricted to simple batch epidemics with transition rates given by [α²X_n(t){n −X_n(t) +X_n(0)}]^1/2, t[0, ∞), α(0, ∞). This class of simple batch epidemics generalizes a model used and motivated by McNeil (1972) to describe simple epidemic situations. It is shown for this class of simple batch epidemics, that X_n(t), with suitable standardization, converges in distribution as n→∞ to a normal random variable for all t(0, t₀), and t₀ is evaluated.     

A model of the complex response of Staphylococcus aureus to methicillin

It is widely accepted that β-lactam antimicrobials cause cell death through a mechanism that interferes with cell wall synthesis. Later studies have also revealed that β-lactams modify the autolysis function (the natural process of self-exfoliation of the cell wall) of cells. The dynamic equilibrium between growth and autolysis is perturbed by the presence of the antimicrobial. Studies with Staphylococcus aureus to determine the minimum inhibitory concentration (MIC) have revealed complex responses to methicillin exposure. The organism exhibits four qualitatively different responses: homogeneous sensitivity, homogeneous resistance, heterogeneous resistance and the so-called ‘Eagle-effect’. A mathematical model is presented that links antimicrobial action on the molecular level with the overall response of the cell population to antimicrobial exposure. The cell population is modeled as a probability density function F(x,t) that depends on cell wall thickness x and time t. The function F(x,t) is the solution to a Fokker-Planck equation. The fixed point solutions are perturbed by the antimicrobial load and the advection of F(x,t) depends on the rates of cell wall synthesis, autolysis and the antimicrobial concentration. Solutions of the Fokker-Planck model are presented for all four qualitative responses of S. aureus to methicillin exposure.     

Current–voltage–time records of ion translocating enzymes

Membrane currents, as non-linear functions of membrane voltage, V, and time, t, can be recorded quickly by triangular V protocols. From the differences, dI(V,t), of these relationships upon addition of a putative substrate of a charge-translocating membrane protein, the I(V,t) relationships of the transporter itself can be determined. These relationships likely comprise a steady-state component, I_a(V), of the active transporter, and a dynamic component, p_a(V,t), of its V- and time-dependent activity, p_a. Here, the steady-state component is modeled by a central reaction cycle, which senses a fraction _tr of the total V, whereas 1–_tr can be assigned to an inner and outer pore section with _i and _o, respectively (_i+_tr+_o = 1). For the enzymatic cycle, fast binding/debinding is assumed, plus V-sensitive and -insensitive reaction steps which may become rate limiting for charge translocation. At given substrate concentrations, I_a(V) is defined by eight independent system parameters, including a coefficient for the barrier shape of charge translocation. In ordinary cases, the behavior of p_a(V,t) can be described by two rate constants (for activation and inactivation) and their respective V-sensitivity coefficients. Here, the effects of the individual system parameters on I(V,t) from triangular V-clamp experiments are investigated systematically. The results are illustrated by panels of typical curve shapes for non-gated and gated transporters to enable a first classification of mechanisms. We demonstrate that all system parameters can be determined fairly well by fitting the model to experimental data of known origin. Applicability of the model to channels, pumps and cotransporters is discussed.     

Electrical noise from lipid bilayer membranes in the presence of hydrophobic ions

Summary In the presence of the hydrophobic ion dipicrylamine, lipid bilayer membranes exhibit a characteristic type of noise spectrum which is different from other forms of noise described so far. The spectral density of current noise measured at zero voltage increases in proportion to the square of frequency at low frequencies and becomes constant at high frequencies. The observed form of the noise spectrum can be interpreted on the basis of a transport model for hydrophobic ions in which it is assumed that the ions are adsorbed in potential-energy minima at either membrane surface and are able to cross the central energy barrier by thermal activation. Accordingly, current-noise results from random fluctuations in the number of ions jumping over the barrier from right to left and from left to right. On the basis of this model the rate constantk _i for the translocation of the hydrophobic ion across the barrier, as well as the mean surface concentrationN _t of adsorbed ions may be caluculated from the observed spectral intensity of current noise. The values ofk _i obtained in this way closely agree with the results of previous relaxation experiments. A similar, although less quantitative, agreement is also found for the surface concentrationN _t .     

The Discrete Distribution Used for the Log-rank Test Can Be Inaccurate

When the number of tumors is small, a significance level for the Cox-Mantel (log-rank) test Z is often computed using a discrete approximation to the permutation distribution. For j = 0,…, J let N_j(t) be the number of animals in group j alive and tumor-free at the start of time t. Make a 2 × (1+J) table for each time t of the number of animals R_j(t) with newly palpated tumor out of the total N_j(t) at risk. There are a total of say K tables, one for each distinct time t with observed death or newly palpated tumor. The usual discrete approximation to the permutation distribution of Z is defined by taking tables to be independent with fixed margins N_j(t) and ΣR_j(t) for all t. However, the N_j(t) are random variables for the actual permutation distribution of Z, resulting in dependence among the tables. Calculations for the exact permutation distribution are explained, and examples are given where the exact significance level differs substantially from the usual discrete approximation. The discrepancy arisis primarily because permutations with different Z-scores under the exact distribution can be equal for the discrete approximation, inflating the approximate P-value.     

Chemotactic collapse for the Keller-Segel model

 This work is concerned with the system (S) {u _t =Δu − χ∇ (u∇v) for x∈Ω, t>0Γ v _t =Δv+(u−1) for x∈Ω, t>0 where Γ, χ are positive constants and Ω is a bounded and smooth open set in ℝ². On the boundary ∂Ω, we impose no-flux conditions: (N) ∂u∂n =∂v∂n =0 for x∈∂ Ω, t>0 Problem (S), (N) is a classical model to describe chemotaxis corresponding to a species of concentration u(x, t) which tends to aggregate towards high concentrations of a chemical that the species releases. When completed with suitable initial values at t=0 for u(x, t), v(x, t), the problem under consideration is known to be well posed, locally in time. By means of matched asymptotic expansions techniques, we show here that there exist radial solutions exhibiting chemotactic collapse. By this we mean that u(r, t) →Aδ(y) as t→T for some T<∞, where A is the total concentration of the species. Received 9 March 1995; received in revised form 25 December 1995     

An epidemic model in a patchy environment

An epidemic model is proposed to describe the dynamics of disease spread among patches due to population dispersal. We establish a threshold above which the disease is uniformly persistent and below which disease-free equilibrium is locally attractive, and globally attractive when both susceptible and infective individuals in each patch have the same dispersal rate. Two examples are given to illustrate that the population dispersal plays an important role for the disease spread. The first one shows that the population dispersal can intensify the disease spread if the reproduction number for one patch is large, and can reduce the disease spread if the reproduction numbers for all patches are suitable and the population dispersal rate is strong. The second example indicates that a population dispersal results in the spread of the disease in all patches, even though the disease can not spread in each isolated patch.     

A mathematical theory of the functional dynamics of cortical and thalamic nervous tissue

It is proposed that distinct anatomical regions of cerebral cortex and of thalamic nuclei are functionally two-dimensional. On this view, the third (radial) dimension of cortical and thalamic structures is associated with a redundancy of circuits and functions so that reliable signal processing obtains in the presence of noisy or ambiguous stimuli.A mathematical model of simple cortical and thalamic nervous tissue is consequently developed, comprising two types of neurons (excitatory and inhibitory), homogeneously distributed in planar sheets, and interacting by way of recurrent lateral connexions. Following a discussion of certain anatomical and physiological restrictions on such interactions, numerical solutions of the relevant non-linear integro-differential equations are obtained. The results fall conveniently into three categories, each of which is postulated to correspond to a distinct type of tissue: sensory neo-cortex, archior prefrontal cortex, and thalamus.The different categories of solution are referred to as dynamical modes. The mode appropriate to thalamus involves a variety of non-linear oscillatory phenomena. That appropriate to archior prefrontal cortex is defined by the existence of spatially inhomogeneous stable steady states which retain contour information about prior stimuli. Finally, the mode appropriate to sensory neo-cortex involves active transient responses. It is shown that this particular mode reproduces some of the phenomenology of visual psychophysics, including spatial modulation transfer function determinations, certain metacontrast effects, and the spatial hysteresis phenomenon found in stereopsis.List of Symbols (t) Post-synaptic membrane potential (psp) - Maximum amplitude of psp - t Time - The neuronal membrane time constant - Threshold value of membrane potential - r Absolute refractory period - Synaptic operating delay - v Velocity of propagation of action potentil - x Cartesian coordinate - _jj (x) The probability that cells of class j are connected with cells of class j a distance x away - b _jj The mean synaptic weight of synapses of the jj-th class at x - _jj The space constant for connectivity - _e Surface density of excitatory neurons in a one-dimensional homogeneous and isotropic tissue - _i Surface density of inhibitory neurons in a one-dimensional homogeneous and isotropic tissue - E(x, t) Excitatory Activity, proportion of excitatory cells becoming active per unit time at the instant t, at the point x - I(x, t) Inhibitory Activity, proportion of inhibitory cells becoming active per unit time at the instant t, at the point x - x A small segment of tissue - t A small interval of time - P(x, t) Afferent excitation or inhibition to excitatory neurons - Q(x, t) Afferent excitation or inhibition to inhibitory neurons - N _e (x, t) Mean integrated excitation generated within excitatory neurons at x - N _i (x, t) Mean integrated excitation generated within inhibitory neurons at x - _e [N _e ] Expected proportion of excitatory neurons receiving at least threshold excitation per unit time, as a function of N _e - _i [N _i ] Expected proportion of inhibitory neurons receiving at least threshold excitation per unit time, as a function of N _i - G( _e ) Distribution function of excitatory neuronal thresholds - G( ₁ ) Distribution function of inhibitory neuronal thresholds - ₁ A fixed value of neuronal threshold - h(N _e ; ₁) Proportion per unit time of excitatory neurons at x reaching ₁ with a mean excitation N _e - 1[ ] Heaviside's step-function - R _e (x, t) Number of excitatory neurons which are sensitive at the instant t - R _i (x, t) Number of inhibitory neurons which are sensitive at the instant t - R _e Refractory period of excitatory neurons - r _i Refractory period of inhibitory neurons - E(x, t) Time coarse-grained excitatory activity - I(x, t) Time coarse-grained inhibitory activity - Spatial convolution - Threshold of a neuronal aggregate - v Sensitivity coefficient of response of a neuronal aggregate - E(t) Time coarse-grained spatially localised excitatory activity - I(t)> Time coarse-grained spatially localised inhibitory activity - L ₁,L ₂,L,Q See § 2.2.1, § 2.2.7, § 3.1 - Velocity with which retinal images are moved apart - Stimulus width - E _o, I _o Spatially homogeneous steady states of neuronal activity - k _e ,k _ij S _e S _ij See § 5.1     

Analysis of a mathematical model for the growth of tumors

 In this paper we study a recently proposed model for the growth of a nonnecrotic, vascularized tumor. The model is in the form of a free-boundary problem whereby the tumor grows (or shrinks) due to cell proliferation or death according to the level of a diffusing nutrient concentration. The tumor is assumed to be spherically symmetric, and its boundary is an unknown function r=s(t). We concentrate on the case where at the boundary of the tumor the birth rate of cells exceeds their death rate, a necessary condition for the existence of a unique stationary solution with radius r=R ₀ (which depends on the various parameters of the problem). Denoting by c the quotient of the diffusion time scale to the tumor doubling time scale, so that c is small, we rigorously prove that (i) lim inf _t→∞ s(t)>0, i.e. once engendered, tumors persist in time. Indeed, we further show that (ii) If c is sufficiently small then s(t)→R ₀ exponentially fast as t→∞, i.e. the steady state solution is globally asymptotically stable. Further, (iii) If c is not “sufficiently small” but is smaller than some constant γ determined explicitly by the parameters of the problem, then lim sup _t→∞ s(t)<∞; if however c is “somewhat” larger than γ then generally s(t) does not remain bounded and, in fact, s(t)→∞ exponentially fast as t→∞. Received: 25 February 1998 / Revised version: 30 April 1998     

A general model for stochastic SIR epidemics with two levels of mixing

This paper is concerned with a general stochastic model for susceptible→infective→removed epidemics, among a closed finite population, in which during its infectious period a typical infective makes both local and global contacts. Each local contact of a given infective is with an individual chosen independently according to a contact distribution ‘centred’ on that infective, and each global contact is with an individual chosen independently and uniformly from the whole population. The asymptotic situation in which the local contact distribution remains fixed as the population becomes large is considered. The concepts of local infectious clump and local susceptibility set are used to develop a unified approach to the threshold behaviour of this class of epidemic models. In particular, a threshold parameter R_* governing whether or not global epidemics can occur, the probability that a global epidemic occurs and the mean proportion of initial susceptibles ultimately infected by a global epidemic are all determined. The theory is specialised to (i) the households model, in which the population is partitioned into households and local contacts are chosen uniformly within an infective’s household; (ii) the overlapping groups model, in which the population is partitioned in several ways, with local uniform mixing within the elements of the partitions; and (iii) the great circle model, in which individuals are equally spaced on a circle and local contacts are nearest-neighbour.     

Generalized stable population theory

In generalizing stable population theory we give sufficient, then necessary conditions under which a population subject to time dependent vital rates reaches an asymptotic stable exponential equilibrium (as if mortality and fertility were constant). If x ₀(t) is the positive solution of the characteristic equation associated with the linear birth process at time t, then rapid convergence of x ₀(t) to x ₀ and convergence of mortality rates produce a stable exponential equilibrium with asymptotic growth rate x ₀–1. Convergence of x ₀(t) to x ₀ and convergence of mortality rates are necessary. Therefore the two sets of conditions are very close. Various implications of these results are discussed and a conjecture is made in the continuous case.     

A mathematical model of biological evolution

In order to understand generally how the biological evolution rate depends on relevant parameters such as mutation rate, intensity of selection pressure and its persistence time, the following mathematical model is proposed: dN _n (t)/dt=(m _n (t-)N _n (t)+N _n-1(t) (n=0,1,2,3...), where N _n (t) and m _n (t) are respectively the number and Malthusian parameter of replicons with step number n in a population at time t and is the mutation rate, assumed to be a positive constant. The step number of each replicon is defined as either equal to or larger by one than that of its parent, the latter case occurring when and only when mutation has taken place. The average evolution rate defined by is rigorously obtained for the case (i) m _n (t)=m _n is independent of t (constant fitness model), where m _n is essentially periodic with respect to n, and for the case (ii) (periodic fitness model), together with the long time average m of the average Malthusian parameter . The biological meaning of the results is discussed, comparing them with the features of actual molecular evolution and with some results of computer simulation of the model for finite populations.An early version of this study was read at the International Symposium on Mathematical Topics in Biological held in kyoto, Japan, on September 11–12, 1978, and was published in its Procedings.