Identification of nonlinear models of biological membranes using the voltage-clamp method

The method for identification of nonlinear systems proposed in 1952 by Hodgkin and Huxley is mathematically justified. A procedure for the application of this method is developed, including the development of the structure of a mathematical model, carrying out a series of tests with special chosen signals, and determination of unknown parameters. Basic requirements for the admissible sets of input and output signals and to the system operator have been determined. It is shown that this operator should be totally continuous and that the minimum number of unknown parameters and the minimum complexity of the operator structure should give an approximation of the necessary quality. The pros and cons of the Hodgkin-Huxley and Noble mathematical models and the methods used for their development are discussed. A structure for the operator for the identification of mathematical models of excitable membranes with a large number of membrane currents is proposed. It is found that the nonlinear electrical properties of biological membranes can be identified using tests with other types of “clamped” parameters, such as the current, ramp voltage, etc.     

Identification of nonlinear models of biological membranes by the method of fixed voltage

A mathematical substantiation of the method suggested by Hodkin and Huxley in 1952 for the identification of nonlinear systems is presented. A procedure for the application of this method was developed, which involves creating the structure of a mathematical model, carrying out a series of tests with specially chosen signals, and finding the unknown parameters. The basic requirements to admissible sets of entrance and target signals and the operator of the system were determined. It was shown that it should be quite continuous, the minimal number of unknown parameters and the minimal complexity of structure of the operator should provide the required quality of approximation. The merits and demerits of the mathematical models of Hodkin-Huxley and Noble, and the procedures used for their creation are discussed. The structure of the operator for the identification of mathematical models of excitable membranes when a large number of membrane currents is considered is offered. It was found that nonlinear electric properties of biological membranes can be identified using tests with other kinds of "fixed" parameters, for example, the method of "fixed" current, the fixed linearly increasing voltage, and others.     

On the impossibility of coexistence of infinitely many strategies

We investigate the possibility of coexistence of pure, inherited strategies belonging to a large set of potential strategies. We prove that under biologically relevant conditions every model allowing for coexistence of infinitely many strategies is structurally unstable. In particular, this is the case when the "interaction operator" which determines how the growth rate of a strategy depends on the strategy distribution of the population is compact. The interaction operator is not assumed to be linear. We investigate a Lotka-Volterra competition model with a linear interaction operator of convolution type separately because the convolution operator is not compact. For this model, we exclude the possibility of robust coexistence supported on the whole real line, or even on a set containing a limit point. Moreover, we exclude coexistence of an infinite set of equidistant strategies when the total population size is finite. On the other hand, for infinite populations it is possible to have robust coexistence in this case. These results are in line with the ecological concept of "limiting similarity" of coexisting species. We conclude that the mathematical structure of the ecological coexistence problem itself dictates the discreteness of the species.     

Modelling the stability of Stx lysogens

Shiga-toxin-converting bacteriophages (Stx phages) are temperate phages of Escherichia coli, and can cause severe human disease. The spread of shiga toxins by Stx phages is directly linked to lysogen stability because toxins are only synthesized and released once the lytic cycle is initiated. Lysogens of Stx phages are known to be less stable than those of the related lambda phage; this is often described in terms of a 'hair-trigger' molecular switch from lysogeny to lysis. We have developed a mathematical model to examine whether known differences in operator regions and binding affinities between Stx phages and lambda phage can account for the lower stability of Stx lysogens. The Stx phage 933W has only two binding sites in its left operator region (compared to three in phage lambda), but this has a minimal effect on 933W lysogen stability. However, the relatively weak binding affinity between repressor molecules and the second binding site in the right operator is found to significantly reduce the stability of its lysogens, and may account for the hair-trigger nature of the switch. Reduced lysogen stability can lead to increased frequency of genetic recombination in bacterial genomes. The development of the mathematical model has considerable utility in understanding the behaviour and evolution of the molecular switch, with implications for phage-related diseases.     

A model of proliferating cell populations with inherited cycle length

A mathematical model of cell population growth introduced by J. L. Lebowitz and S. I. Rubinow is analyzed. Individual cells are distinguished by age and cell cycle length. The cell cycle length is viewed as an inherited property determined at birth. The density of the population satisfies a first order linear partial differential equation with initial and boundary conditions. The boundary condition models the process of cell division of mother cells and the inheritance of cycle length by daughter cells. The mathematical analysis of the model employs the theory of operator semigroups and the spectral theory of linear operators. It is proved that the solutions exhibit the property of asynchronous exponential growth.     

基于遗传算法预测2D三向的蛋白质结构

本文基于范德华力势能预测2D三向的蛋白质结构。首先,将蛋白质结构预测这一生物问题转化为数学问题,并建立基于范德华力势能函数的数学模型。其次,使用遗传算法对数学模型进行求解,为了提高蛋白质结构预测效率,我们在标准遗传算法的基础上引入了调整算子这一概念,改进了遗传算法。最后,进行数值模拟实验。实验的结果表明范德华力势能函数模型是可行的,同时,和规范遗传算法相比,改进后的遗传算法能够较大幅度提高算法的搜索效率,并且遗传算法在蛋白质结构预测问题上有巨大潜力。     

Dynamics and potential impact of the immune response to chronic myelogenous leukemia

Recent mathematical models have been developed to study the dynamics of chronic myelogenous leukemia (CML) under imatinib treatment. None of these models incorporates the anti-leukemia immune response. Recent experimental data show that imatinib treatment may promote the development of anti-leukemia immune responses as patients enter remission. Using these experimental data we develop a mathematical model to gain insights into the dynamics and potential impact of the resulting anti-leukemia immune response on CML. We model the immune response using a system of delay differential equations, where the delay term accounts for the duration of cell division. The mathematical model suggests that anti-leukemia T cell responses may play a critical role in maintaining CML patients in remission under imatinib therapy. Furthermore, it proposes a novel concept of an “optimal load zone” for leukemic cells in which the anti-leukemia immune response is most effective. Imatinib therapy may drive leukemic cell populations to enter and fall below this optimal load zone too rapidly to sustain the anti-leukemia T cell response. As a potential therapeutic strategy, the model shows that vaccination approaches in combination with imatinib therapy may optimally sustain the anti-leukemia T cell response to potentially eradicate residual leukemic cells for a durable cure of CML. The approach presented in this paper accounts for the role of the anti-leukemia specific immune response in the dynamics of CML. By combining experimental data and mathematical models, we demonstrate that persistence of anti-leukemia T cells even at low levels seems to prevent the leukemia from relapsing (for at least 50 months). As a consequence, we hypothesize that anti-leukemia T cell responses may help maintain remission under imatinib therapy. The mathematical model together with the new experimental data imply that there may be a feasible, low-risk, clinical approach to enhancing the effects of imatinib treatment.     

An application of Lacker's mathematical model for the prediction of ovarian response to superstimulation

Introduction. A mathematical model of ovarian follicular growth is applied to the problem of predicting ovarian response in a superstimulation protocol. Methods. Fifty-four women enrolled in an ovarian superstimulation program of therapy for the amelioration of idiopathic infertility had their ovarian cycles synchronized by taking Demulen 30 for two weeks prior to the study. Daily ultrasonographic imaging, measurements of serum estradiol and doses of hMG began on day 5 after the patients stopped taking Demulen. The diameters of individual follicles were measured and followed daily. When the largest follicle attained a diameter of 19 mm, hCG was given to induce ovulation. Individual follicle growth data were fit to a mathematical model of ovarian follicle maturation and the resulting parameters were used to classify patients into low and high ovarian response groups. Results. The parameters computed from the mathematical model fit were found to be predictive of ovarian response with a sensitivity of 71% and a specificity of 70%. The parameters were also meaningful within the context of the original mathematical model and have value for determining how doses of hMG may be adjusted during the course therapy to increase the ovarian response in individuals. Conclusion. Mathematical modeling of ultrasonographically derived follicular growth data has significant potential for clinical application in ovarian superstimulation protocols. The method of fitting follicular growth data to a mathematical follicle maturation surface furthermore provides a straightforward approach for the characterization of ovarian follicular dynamics in general.     

Mlab--a mathematical modeling tool

An interactive interpreter called Mlab is described. One uses Mlab by typing commands. In this sense, Mlab is a programming language. It has various mathematical and graphical facilities which make it a useful tool for mathematical modeling. The curve fitting capabilities of Mlab are augmented with differential-equation-handling and matrix-manipulation capabilities which provide a powerful and civilized facility for curve fitting. Many people are engaged in this activity, and, in general, they use programs which are neither sufficiently general nor easy to use. (Some conventional programming is usually required, for example.) Mlab purports to be easier than alternate approaches. The nature of Mlab is discussed with accompanying examples. The main example is the use of curve fitting to determine molecular weight from ultracentrifuge data. This example was chosen because it exhibits a special feature of Mlab, namely the root operator, which appears in the definition of the model function.     

Model-based optimization of a conductive matrix enzyme electrode

A mathematical model has been developed to describe the mechanism for internal mass transfer and enzyme reaction kinetics of an amperometric conductive matrix enzyme electrode. The model is simplified and solved analytically to arrive at a representation for the response slope in the linear range as well as for the response time. This is the first time that the response time of an enzyme electrode is described by a mathematical model. Simulations give information on how the design parameters influence the performance of the electrode for a glucose oxidase catalyzed sensing reaction process. Based on this information, several designs were constructed and tested showing suitable agreement with theoretical predictions. Finally, an optimized electrode was designed and validated.     

A Mathematical Model for the Effects of HER2 Overexpression on Cell Proliferation in Breast Cancer

We present a mathematical model to study the effects of HER2 over-expression on cell proliferation in breast cancer. The model illustrates the proliferative behavior of cells as a function of HER2 and EGFR receptors numbers, and the growth factor EGF. This mathematical model comprises kinetic equations describing the cell surface binding of EGF growth factor to EGFR and HER2 receptors, coupled to a model for the dependence of cell proliferation rate on growth factor receptors binding. The simulation results from this model predict: (1) a growth advantage associated with excess HER2 receptors; (2) that HER2-over-expression is an insufficient parameter to predict the proliferation response of cancer cells to epidermal growth factors; and (3) the EGFR receptor expression level in HER2-over-expressing cells plays a key role in mediating the proliferation response to receptor-ligand signaling. This mathematical model also elucidates the interaction and roles of other model parameters in determining cell proliferation rate of HER2-over-expressing cells.     

Response of a pacemaker neuron model to stochastic pulse trains

 We investigated the response of a pacemaker neuron model to trains of inhibitory stochastic impulsive perturbations. The model captures the essential aspect of the dynamics of pacemaker neurons. Especially, the model reproduces linearization by stochastic pulse trains, that is, the disappearance of the paradoxical segments in which the output firing rate of pacemaker neurons increases with inhibition rate, as the coefficient of variation of the input pulse train increases. To study the response of the model to stochastic pulse trains, we use a Markov operator governing the phase transition. We show how linearization occurs based on the spectral analysis of the Markov operator. Moreover, using Lyapunov exponents, we show that variable inputs evoke reliable firing, even in situations where periodic stimulation with the same mean rate does not. Received: 30 April 2001 / Accepted in revised form: 19 September 2001