考虑CTL免疫饱和作用及自我增殖的HIV模型的性态分析

建立了具有CTL免疫饱和作用及自我增殖的病毒动力学模型,通过构造Lyapunov函数,得到了模型的全局稳定性.特别地,当CTL自我增殖率比较大时,如果基本再生数小于阈值,无感染但免疫激活平衡点始终存在且全局渐近稳定,这意味着在HIV感染中虽然病毒被清除,但免疫效应始终存在.而当CTL自我增殖率不足时,免疫饱和效应可能导致免疫功能受到抑制.进一步通过数值模拟发现,忽略免疫饱和作用会过高估计免疫效应.     

具有群体效应和时滞的交叉扩散捕食-食饵系统的Hopf分支（英文）

本文研究具有群体效应和时滞的交叉扩散捕食-食饵模型的Hopf分支.将死亡率β和时滞τ作为分支因子,通过分析特征方程,讨论系统正平衡点E_3(u~*,v~*)的稳定性和Hopf分支的存在性.我们得到当参数值穿过临界值时,该系统会在正平衡点E_3附近产生Hopf分支.最后,我们进行数值模拟,验证了结论的正确性.     

具有免疫时滞、饱和CTL免疫反应和免疫损害的HTLV-I感染模型的动力学性态

该文研究一类具有免疫时滞、饱和CTL免疫反应和免疫损害的HTLV-I感染动力学模型.通过计算得到了免疫未激活再生率和免疫激活再生率.通过构造适当的Lyapunov泛函,并利用LaSalle不变性原理,证明了当免疫未激活再生率小于1时,病毒未感染平衡点是全局渐近稳定的;当免疫激活再生率小于1且免疫未激活再生率大于1时,免疫未激活平衡点是全局渐近稳定的;在免疫时滞为0的情形下,当免疫激活再生率大于1时,免疫激活平衡点是全局渐近稳定的.当免疫时滞大于某一临界值时,给出了免疫激活平衡点处产生Hopf分支的条件.最后,通过数值模拟对理论结果进行了说明.     

不完全免疫的多阶段绵羊布鲁氏菌病模型的稳定性分析与最优控制

本文研究一个多阶段的不完全免疫的布鲁氏菌病模型,得到模型平衡点的存在唯一性以及基本再生数R₀.通过构造合适的Lyapunov函数,证明了无病平衡点与地方病平衡点的全局渐近稳定性.在考虑控制成本的情况下,利用最优控制理论得到了布鲁氏菌病最优控制策略.最后用数值模拟验证了理论结果.     

一类HIV病毒动力学模型的全局稳定性与周期解

建立了一类较广泛的HIV感染CD4+T细胞病毒动力学模型,给出了一个感染细胞在其整个感染期内产生的病毒的平均数(基本再生数)R0的表达式,运用Lyapunov原理和Routh-Hurwitz判据得到了该模型的未感染平衡点与感染平衡点的存在性与稳定性条件.同时也得到了模型存在轨道渐近稳定周期解和系统持续生存的条件,并通过数值模拟验证了所得到的结果.     

具有乘积型Allee效应的Leslie-Gower捕食模型的动力学

在Leslie-Gower捕食模型中引入乘积型Allee效应,并分析模型的性质.首先,模型存在正向不变集,解是一致有界的.其次,讨论了平衡点存在和稳定的条件,并利用Liapunov函数方法得到正平衡点全局渐近稳定的充分条件.最后,根据Hopf分岔定理分析了分岔现象出现的条件和在这个过程中产生的极限环.     

一类肿瘤-免疫系统动力学性态的全局分析

基于肿瘤细胞对免疫效应细胞的增殖有刺激和抑制两方面作用的事实,将其综合作用以可正可负的作用率系数来描述.完整分析了肿瘤细胞与效应细胞相互作用的动力学模型的全局性态,发现了该模型会发生鞍结点分支和双稳定现象,使得肿瘤增殖的最终结果依赖于初始状态,并且得到了相应的阈值条件.同时,还具体分析了效应细胞的固有输入率与肿瘤细胞对效应细胞的作用率系数对模型动力学性态的影响.所得结果显示当肿瘤细胞对效应细胞的抑制作用足够强时,模型会有复杂的动力学性态.     

一类带有接种的流行病模型的全局稳定性

该文讨论了一类带有接种的流行病模型. 在该模型中假设恢复后的个体与被接种的个体均具有确定的免疫期, 它是一个时滞微分系统. 通过分析, 得到了地方病平衡点存在的阈值, 以及无病平衡点和地方病平衡点局部渐近稳定和全局渐近稳定的充分条件.     

无标度网络上带有时滞的SIRS模型的稳定性分析

建立了一个无标度网络上带有时滞的SIRS模型,并分析了在度不相关情况下模型的动力学性态.当基本再生数R_01时,模型只有无病平衡点,运用Jacobi矩阵和Lyapunov泛函得出无病平衡点的全局稳定性;当R_01时,无病平衡点不稳定,存在唯一地方病平衡点且是持续的.     

含有免疫策略的一类计算机病毒模型稳定性分析

建立计算机病毒的动力学数学模型,可以更好地揭示计算机病毒流行的原因.考虑到免疫策略对计算机病毒动力学模型的重要影响,对一类SEIR模型进行了改进.得到了模型的无感染平衡点和感染平衡点的存在性,并分别求得两类平衡点的渐近稳定条件,通过数值模拟展示了理论成果.     

Modelling the human immune response dynamics during progression from Mycobacterium latent infection to disease

The use of mathematical tools to study biological processes is of necessity in determining the effects of these biological processes occurring at different levels. In this paper, we study the immune system’s response to infection with the bacteria Mycobacterium tuberculosis (the causative agent of tuberculosis). The response by the immune system is either global (lymph node, thymus, and blood) or local (at the site of infection). The response by the immune system against tuberculosis (TB) at the site of infection leads to the formation of spherical structures which comprised of cells, bacteria, and effector molecules known as granuloma. We developed a deterministic model capturing the dynamics of the immune system, macrophages, cytokines and bacteria. The hallmark of Mycobacterium tuberculosis (MTB) infection in the early stages requires a strong protective cell-mediated naive T cells differentiation which is characterised by antigen-specific interferon gamma (IFN-γ). The host immune response is believed to be regulated by the interleukin-10 cytokine by playing the critical role of orchestrating the T helper 1 and T helper 2 dominance during disease progression. The basic reproduction number is computed and a stability analysis of the equilibrium points is also performed. Through the computation of the reproduction number, we predict disease progression scenario including the latency state. The occurrence of latent infection is shown to depend on a number of effector function and the bacterial load for R₀ < 1. The model predicts that endemically there is no steady state behaviour; rather it depicts the existence of the MTB to be a continuous process progressing over a differing time period. Simulations of the model predict the time at which the activated macrophages overcome the infected macrophages (switching time) and observed that the activation rate (ω) correlates negatively with it. The efficacy of potential host-directed therapies was determined by the use of the model.     

Bifurcation analysis of a delayed mathematical model for tumor growth

In this study, we present a modified mathematical model of tumor growth by introducing discrete time delay in interaction terms. The model describes the interaction between tumor cells, healthy tissue cells (host cells) and immune effector cells. The goal of this study is to obtain a better compatibility with reality for which we introduced the discrete time delay in the interaction between tumor cells and host cells. We investigate the local stability of the non-negative equilibria and the existence of Hopf-bifurcation by considering the discrete time delay as a bifurcation parameter. We estimate the length of delay to preserve the stability of bifurcating periodic solutions, which gives an idea about the mode of action for controlling oscillations in the tumor growth. Numerical simulations of the model confirm the analytical findings.     

Dynamics of Coupled Cell Networks: Synchrony, Heteroclinic Cycles and Inflation

We consider the dynamics of small networks of coupled cells. We usually assume asymmetric inputs and no global or local symmetries in the network and consider equivalence of networks in this setting; that is, when two networks with different architectures give rise to the same set of possible dynamics. Focussing on transitive (strongly connected) networks that have only one type of cell (identical cell networks) we address three questions relating the network structure to dynamics. The first question is how the structure of the network may force the existence of invariant subspaces (synchrony subspaces). The second question is how these invariant subspaces can support robust heteroclinic attractors. Finally, we investigate how the dynamics of coupled cell networks with different structures and numbers of cells can be related; in particular we consider the sets of possible “inflations” of a coupled cell network that are obtained by replacing one cell by many of the same type, in such a way that the original network dynamics is still present within a synchrony subspace. We illustrate the results with a number of examples of networks of up to six cells.     

Localization of compact invariant sets and global stability in analysis of one tumor growth model

In this paper, we study some features of global behavior of the four‐dimensional superficial bladder cancer model with Bacillus Calmette‐Guérin (BCG) immunotherapy described by Bunimovich‐Mendrazitsky et al. in 2007 with the help of localization analysis of its compact invariant sets. Its dynamics is defined by the BCG treatment and by densities of three cells populations: effector cells, tumor infected cells by BCG, and tumor uninfected cells. We find upper bounds for ultimate dynamics of the whole state vector in the positive orthant and also under condition that there are no uninfected tumor cells. Further, we prove the existence of the bounded positively invariant domain in both of these two situations. Finally, by using these assertions, we derive our main result: sufficient conditions of global asymptotic stability of the tumor‐free equilibrium point in the positive orthant. Copyright © 2013 John Wiley & Sons, Ltd.     

Generals die in friendly fire,or modeling immune response to HIV

We develop a kinetic model for CD8 T lymphocytes (CTL) whose purpose is to kill cells infected with viruses and intracellular parasites. Using a set of first-order nonlinear differential equations, the model predicts how numbers of different cell types involved in CTL response depend on time. The model postulates that CTL response requires continuous presence of professional antigen-presenting cells (APC) comprised of macrophages and dendritic cells. It assumes that any virus present in excess of a threshold level activates APC that, in turn, activate CTL that expand in number and become armed “effector” cells. In the end, APC are deactivated after virus is cleared. The lack of signal from APC causes effector cells to differentiate, by default, into “transitory cells” that either die, or, in a small part, become long-lived memory cells. Viruses capable of infecting APC will cause premature retirement of effector CTL. If transitory cells encounter virus, which takes place after the premature depletion, CTL become anergic (unresponsive to external stimuli). The model is designed to fit recent experiments on primary CTL response to simian immunodeficiency virus closely related to HIV and lymphocytic choriomeningitis virus. The two viruses are known to infect APC and make them targets for CTL they are supposed to control. Both viruses cause premature depletion and anergy of CTL and persist in the host for life.     

Environmentally induced amplitude death and firing provocation in large-scale networks of neuronal systems

We study the dynamics of multielement neuronal systems taking into account both the direct interaction between the cells via linear coupling and nondiffusive cell-to-cell communication via common environment. For the cells exhibiting individual bursting behavior, we have revealed the dependence of the network activity on its scale. Particularly, we show that small-scale networks demonstrate the inability to maintain complicated oscillations: for a small number of elements in an ensemble, the phenomenon of amplitude death is observed. The existence of threshold network scales and mechanisms causing firing in artificial and real multielement neural networks, as well as their significance for biological applications, are discussed.     

二阶Hopfield神经网络周期解的存在性

本文讨论了一类带有时滞的二阶Hopfield神经网络周期解的存在性问题。首先利用Brouwer不动点定理证明了平衡点的存在性,通过平衡点和拉格朗日中值定理,将高阶神经网络模型转换为一阶模型,然后利用重合度理论给出了周期解存在的充分条件。     

Social network topology: a Bayesian approach

Elucidating the pattern of links within social networks is a challenging problem. Of particular difficulty is determining the existence of links in those groups that take active measures to conceal their internal connections, such as terrorist or criminal organizations where conventional social network analysis data-gathering techniques cannot be applied. Network representations of such organizations are useful, because they often represent a useful point of departure in thinking both about the potential capabilities of organizations and how to conduct effective measures to counter them. Developing an effective process for constructing such network representations from incomplete and limited data of variable quality is a topic of much current interest. Here, a method based on Bayesian inference is presented that probabilistically infers the existence of links within a social network. It is tested on data from open source publications. Additionally, the method represents a possible approach to dynamically modelling networks, as it is feasible to calculate how a network will reconfigure following an intervention.     

DYNAMIC BEHAVIOURS OF AN AUTONOMOUS STAGE-STRUCTURED COMPETITIVE SYSTEMS WITH TOXIC EFFECT

It is known that somebody''s behavior (decision) in a stochastic social network may be influenced by that of his (or her) friends. In this paper, we consider two stochastic social network game models (a) and (b) which can be defined respectively by two different utility functions. Some sufficient conditions for the existence of Nash equilibrium (NE) of the two network game models are obtained by analyzing the different effort relation between a player and his (or her) neighbors.     

Learning dynamics in second order networks

A model of a second order neural network incorporating a weight adjusting learning component and time delays in processing and transmission is formulated. Delay-independent sufficient conditions are derived for the existence of an asymptotically and exponentially stable equilibrium state. The learning dynamics is modelled with an unsupervised Hebbian-type learning algorithm together with a forgetting term. If there is nothing for the network to learn or if there are no second order synaptic interactions, then our analysis will correspond to one of the standard model of neural networks.