arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新，休息一下吧，周末愉快~~
arXiv 1209.3820q-bio.PEq-bio.QM
收藏

免疫反应的群体表达模型

Population-expression models of immune response

Sean P Stromberg, Rustom Antia, Ilya Nemenman

备注：
Revised manuscript with an additional included Supplemental. The Supplemental contains two contrasting derivations of the population-expression PDE formulation, one from a fluid-dynamics perspective using the divergence theorem, the other from a statistical physics/systems-biology perspective using a chemical master equation
发表信息：
Physical biology 10 (3), 035010, 2013
阅读 PDF
arXiv 页面 ↗HTML 原文 ↗

摘要：

对病原体的免疫反应有两个基本特征。第一种是一些病原体特异性细胞的扩增，以形成足够大的群体来控制病原体。第二个是细胞从初始幼稚表型分化为控制病原体的效应表型，随后分化为维持并负责长期保护的记忆表型的过程。扩张和分化在很大程度上是独立考虑的。细胞群体的变化通常使用基于生态学的常微分方程模型来描述。相反，单细胞的分化在系统生物学中进行研究，并且经常通过考虑单个细胞中基因和蛋白质表达的变化来建模。实验系统生物学的最新进展首次提供了数据，以允许在感染期间耦合免疫细胞的群体和高维表达数据。在这里，我们描述和开发的人口表达模型，这两个过程集成到系统生物学的多细胞水平。当转化为数学方程时，这些模型导致非保守的、非局部的对流扩散方程。我们描述的情况下，人口表达的方法可以从数据中做出正确的推断，而以前的建模方法的基础上共同的简化假设会失败。我们还探讨了如何使用模型简化技术来构建种群表达模型，在保持系统基本特征的同时最大限度地降低模型的复杂性。虽然我们在本文中考虑免疫学问题，但我们希望群体表达模型具有更广泛的适用性。

英文摘要：

The immune response to a pathogen has two basic features. The first is the expansion of a few pathogen-specific cells to form a population large enough to control the pathogen. The second is the process of differentiation of cells from an initial naive phenotype to an effector phenotype which controls the pathogen, and subsequently to a memory phenotype that is maintained and responsible for long-term protection. The expansion and the differentiation have been considered largely independently. Changes in cell populations are typically described using ecologically based ordinary differential equation models. In contrast, differentiation of single cells is studied within systems biology and is frequently modeled by considering changes in gene and protein expression in individual cells. Recent advances in experimental systems biology make available for the first time data to allow the coupling of population and high dimensional expression data of immune cells during infections. Here we describe and develop population-expression models which integrate these two processes into systems biology on the multicellular level. When translated into mathematical equations, these models result in non-conservative, non-local advection-diffusion equations. We describe situations where the population-expression approach can make correct inference from data while previous modeling approaches based on common simplifying assumptions would fail. We also explore how model reduction techniques can be used to build population-expression models, minimizing the complexity of the model while keeping the essential features of the system. While we consider problems in immunology in this paper, we expect population-expression models to be more broadly applicable.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

↑