论文标题
T-淋巴细胞,B细胞和树突状细胞的免疫系统反应的HIV感染动态模型:综述
Dynamic model of HIV infection with immune system response of T-lymphocytes, B-cells and dendritic cells: a review
论文作者
论文摘要
非细胞时间依赖性的普通微分方程(ODE)的动态模型已应用于HIV感染与免疫系统细胞的相互作用。该模型已被简化为两个隔室:淋巴结和外周血。该模型包括几种状态下的CD4 T淋巴细胞(静止Q,幼稚N和活化的T),细胞毒性CD8 T细胞,B细胞和树突状细胞。该模型中还包括了针对每种抗原特异的细胞因子和免疫球蛋白(即GP41或P24),对CD4 T细胞对感染区域的影响效应进行建模,并通过免疫球蛋白降低病毒浓度。 CD4 T淋巴细胞的HIV病毒感染以几个阶段进行建模:在融合为HIV连接(H)和融合后,融合为非腐败 /流产性感染(M),并允许 /延迟感染(L)和允许 /允许 /允许 /主动 /主动感染(I)。这些方程已在称为Immune System App的C ++/Python接口应用程序中实现,该应用程序运行了开放的Modelica软件,以通过第四阶Runge-Kutta数值近似求解ODE系统。模拟的结果表明,尽管两个隔室中的HIV病毒浓度低于$ 10^{ - 10} $病毒/$μl$ T = 2年后,静态淋巴细胞达到平衡,其浓度低于初始条件,这是由于潜伏期状态而在病毒生产中作为储量。总而言之,该模型可以在其他疾病(例如抗病毒疗法)中提供可靠的结果。
A dynamic model of non-lineal time-dependent ordinary differential equations (ODE) has been applied to the interactions of a HIV infection with the immune system cells. This model has been simplified into two compartments: lymph node and peripheral blood. The model includes CD4 T-lymphocytes in several states (quiescent Q, naive N and activated T), cytotoxic CD8 T-cells, B-cells and dendritic cells. Cytokines and immunoglobulins specific for each antigen (i.e. gp41 or p24) have been also included in the model, modelling the atraction effect of CD4 T-cells to the infected area and the reduction of virus concentration by immunoglobulins. HIV virus infection of CD4 T-lymphocytes is modelled in several stages: before fusion as HIV-attached (H) and after fusion as non-permissive / abortively infected (M), and permissive / latently infected (L) and permissive / actively infected (I). These equations have been implemented in a C++/Python interface application, called Immune System app, which runs Open Modelica software to solve the ODE system through a 4th order Runge-Kutta numerical approximation. Results of the simulation show that although HIV virus concentration in both compartments is lower than $10^{-10}$ virus/$μL$ after t=2 years, quiescent lymphocytes reach an equilibrium with a concentration lower than the initial conditions, due to the latency state, which serves as a reservoir in time of virus production. As a conclusion, this model can provide reliable results in other conditions, such as antiviral therapies.