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适应性免疫系统的随机阶段结构建模

Stochastic stage-structured modeling of the adaptive immune system.

作者信息

Chao Dennis L, Davenport Miles P, Forrest Stephanie, Perelson Alan S

机构信息

Department of Computer Science, University of New Mexico, Albuquerque, 87131, USA.

出版信息

Proc IEEE Comput Soc Bioinform Conf. 2003;2:124-31.

Abstract

We have constructed a computer model of the cytotoxic T lymphocyte (CTL) response to antigen and the maintenance of immunological memory. Because immune responses often begin with small numbers of cells and there is great variation among individual immune systems, we have chosen to implement a stochastic model that captures the life cycle of T cells more faithfully than deterministic models. Past models of the immune response have been differential equation based, which do not capture stochastic effects, or agent-based, which are computationally expensive. We use a stochastic stage-structured approach that has many of the advantages of agent-based modeling but is much more efficient. Our model can provide insights into the effect infections have on the CTL repertoire and the response to subsequent infections.

摘要

我们构建了一个针对抗原的细胞毒性T淋巴细胞(CTL)反应及免疫记忆维持的计算机模型。由于免疫反应通常始于少量细胞,且个体免疫系统之间存在很大差异,我们选择实施一种随机模型,该模型比确定性模型更忠实地捕捉T细胞的生命周期。过去的免疫反应模型要么是基于微分方程的,无法捕捉随机效应;要么是基于主体的,计算成本高昂。我们采用一种随机阶段结构方法,它具有基于主体建模的许多优点,但效率要高得多。我们的模型可以深入了解感染对CTL库的影响以及对后续感染的反应。

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