人类免疫缺陷1型病毒感染CD4（+）T细胞的计算机模拟模型及其分子病理生理学的简要综述
In silico model of infection of a CD4(+) T-cell by a human immunodeficiency type 1 virus, and a mini-review on its molecular pathophysiology
摘要：
导论.人类免疫缺陷病毒1型感染能否诱导T细胞分化状态或过程发生变化？方法.我们将考虑两个随机马尔可夫链模型，一个描述T辅助细胞分化过程，另一个描述T辅助细胞被病毒感染的过程;在这些马尔可夫链中，我们将考虑一组状态$\{X_t \}$，这些状态由参与每个过程及其相互作用的蛋白质组成（细胞的分化或感染），使得我们将获得两个随机转移矩阵（$A,B$），每个进程一个;之后，将执行它们的特征值的计算，其中，如果特征值$λ_i=1$存在，将获得每个矩阵的平衡分布$π^n$的计算，这将告知我们蛋白质之间长期相互作用的趋势。结果所考虑的随机过程具有一个平衡分布，当达到平衡分布时，其信息熵增加，其对数秩分布可建模为离散β广义分布（DGBD）。讨论这两个过程的平衡分布可以被认为是细胞分化良好的状态，因此在T细胞中存在新的HIV依赖性分化状态的诱导;由于它们的DGBD分布，这些过程可以被认为是复杂的过程;由于熵增加，平衡状态是稳定的。结论HIV病毒可以在T细胞中促进一种新的分化状态，这可以解释在患者中观察到的临床特征;尽管如此，该模型没有解释调控网络中涉及的是/否逻辑开关。
英文摘要：
Introduction. Can the infection due to the human immunodeficiency virus type 1 induce a change in the differentiation status or process in T cells?. Methods. We will consider two stochastic Markov chain models, one which will describe the T-helper cell differentiation process, and another one describing that process of infection of the T-helper cell by the virus; in these Markov chains, we will consider a set of states $\{X_t \}$ comprised of those proteins involved in each of the processes and their interactions (either differentiation or infection of the cell), such that we will obtain two stochastic transition matrices ($A,B$), one for each process; afterwards, the computation of their eigenvalues shall be performed, in which, should the eigenvalue $λ_i=1$ exist, the computation for the equilibrium distribution $π^n$ will be obtained for each of the matrices, which will inform us on the trends of interactions amongst the proteins in the long-term. Results. The stochastic processes considered possess an equilibrium distribution, when reaching their equilibrium distribution, there exists an increase in their informational entropy, and their log-rank distributions can be modeled as discrete beta generalized distributions (DGBD). Discussion. The equilibrium distributions of both process can be regarded as states in which the cell is well-differentiated, ergo there exists an induction of a novel HIV-dependent differentiated state in the T-cell; these processes due to their DGBD distribution can be considered complex processes; due to the increasing entropy, the equilibrium states are stable ones. Conclusion. The HIV virus can promote a novel differentiated state in the T-cell, which can give account for clinical features seen in patients; this model, notwithstanding does not give account of YES/NO logical switches involved in the regulatory networks.
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