• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

建立常见实验性流感病毒检测方法的计算机模拟等效物的设计考虑因素。

Design considerations in building in silico equivalents of common experimental influenza virus assays.

机构信息

Department of Physics, Ryerson University, Toronto, ON, Canada.

出版信息

Autoimmunity. 2011 Jun;44(4):282-93. doi: 10.3109/08916934.2011.523267. Epub 2011 Jan 19.

DOI:10.3109/08916934.2011.523267
PMID:21244331
Abstract

Experimentation in vitro is a vital part of the process by which the clinical and epidemiological characteristics of a particular influenza virus strain are determined. We detail the considerations which must be made in designing appropriate theoretical/mathematical models of these experiments and show how modeling can increase the information output of such experiments. Starting from a traditional system of ordinary differential equations, common to infectious disease modeling, we broaden the approach by using an agent-based model, applicable to more general experimental geometries and assumptions about the biological properties of viruses, cell and their interaction. Within this framework, we explore the limits of the assumptions made by more traditional models and the conditions under which these assumptions begin to break down, requiring the use of more sophisticated models. We apply the agent-based model to experimental plaque growth of two influenza strains, one resistant to the antiviral oseltamivir, and extract the values of key infection parameters specific to each strain.

摘要

体外实验是确定特定流感病毒株临床和流行病学特征的过程中至关重要的一部分。我们详细说明了在设计这些实验的适当理论/数学模型时必须考虑的因素,并展示了建模如何增加这些实验的信息输出。从传染病建模中常见的传统常微分方程组开始,我们通过使用适用于更一般实验几何形状和病毒、细胞及其相互作用的生物学特性的基于代理的模型来拓宽方法。在这个框架内,我们探讨了更传统模型所做假设的局限性,以及这些假设开始失效的条件,需要使用更复杂的模型。我们将基于代理的模型应用于两种流感毒株的实验蚀斑生长,并提取每个毒株特有的关键感染参数的值。

相似文献

1
Design considerations in building in silico equivalents of common experimental influenza virus assays.建立常见实验性流感病毒检测方法的计算机模拟等效物的设计考虑因素。
Autoimmunity. 2011 Jun;44(4):282-93. doi: 10.3109/08916934.2011.523267. Epub 2011 Jan 19.
2
Emergence of drug-resistant influenza virus: population dynamical considerations.耐药流感病毒的出现:群体动力学考量
Science. 2006 Apr 21;312(5772):389-91. doi: 10.1126/science.1122947.
3
Inhibition of influenza virus replication by plant-derived isoquercetin.植物源异槲皮苷抑制流感病毒复制。
Antiviral Res. 2010 Nov;88(2):227-35. doi: 10.1016/j.antiviral.2010.08.016. Epub 2010 Sep 6.
4
[Antiviral action of carbonyl-conjugated pentaene macrolides].[羰基共轭五烯大环内酯类的抗病毒作用]
Antibiotiki. 1983 May;28(5):352-7.
5
Influenza viruses resistant to the antiviral drug oseltamivir: transmission studies in ferrets.对抗病毒药物奥司他韦耐药的流感病毒:雪貂传播研究
J Infect Dis. 2004 Nov 1;190(9):1627-30. doi: 10.1086/424572. Epub 2004 Sep 28.
6
Amantadine-oseltamivir combination therapy for H5N1 influenza virus infection in mice.金刚烷胺与奥司他韦联合治疗小鼠H5N1流感病毒感染
Antivir Ther. 2007;12(3):363-70.
7
Assessing the development of oseltamivir and zanamivir resistance in A(H5N1) influenza viruses using a ferret model.使用雪貂模型评估 A(H5N1) 流感病毒中奥司他韦和扎那米韦耐药性的发展。
Antiviral Res. 2010 Sep;87(3):361-6. doi: 10.1016/j.antiviral.2010.06.009. Epub 2010 Jul 21.
8
[The dynamics of multiplication of influenza-virus A-2 in mouse lungs under the influence of 1-aminoadamantan preparations].[1-氨基金刚烷制剂影响下甲型流感病毒A-2在小鼠肺内的增殖动态]
Zentralbl Bakteriol Orig. 1969 Jul;210(3):298-303.
9
Effects of Clinacanthus siamensis leaf extract on influenza virus infection.暹罗鳄嘴花叶子提取物对流感病毒感染的影响。
Microbiol Immunol. 2009 Feb;53(2):66-74. doi: 10.1111/j.1348-0421.2008.00095.x.
10
[The effect of esculamine on experimental influenza].[七叶胺对实验性流感的影响]
Biull Eksp Biol Med. 1966 Jun;61(6):65-8.

引用本文的文献

1
Spatial information allows inference of the prevalence of direct cell-to-cell viral infection.空间信息可用于推断直接细胞间病毒感染的流行情况。
PLoS Comput Biol. 2024 Jul 23;20(7):e1012264. doi: 10.1371/journal.pcbi.1012264. eCollection 2024 Jul.
2
Initial Inoculum and the Severity of COVID-19: A Mathematical Modeling Study of the Dose-Response of SARS-CoV-2 Infections.初始接种量与COVID-19的严重程度:一项关于SARS-CoV-2感染剂量反应的数学建模研究
Epidemiologia (Basel). 2020 Oct 21;1(1):5-15. doi: 10.3390/epidemiologia1010003.
3
Multicellular spatial model of RNA virus replication and interferon responses reveals factors controlling plaque growth dynamics.
多细胞空间 RNA 病毒复制和干扰素反应模型揭示了控制斑块生长动态的因素。
PLoS Comput Biol. 2021 Oct 25;17(10):e1008874. doi: 10.1371/journal.pcbi.1008874. eCollection 2021 Oct.
4
A modular framework for multiscale, multicellular, spatiotemporal modeling of acute primary viral infection and immune response in epithelial tissues and its application to drug therapy timing and effectiveness.一种用于上皮组织中急性原发性病毒感染和免疫反应的多尺度、多细胞、时空建模的模块化框架及其在药物治疗时机和有效性方面的应用。
PLoS Comput Biol. 2020 Dec 21;16(12):e1008451. doi: 10.1371/journal.pcbi.1008451. eCollection 2020 Dec.
5
A modular framework for multiscale, multicellular, spatiotemporal modeling of acute primary viral infection and immune response in epithelial tissues and its application to drug therapy timing and effectiveness: A multiscale model of viral infection in epithelial tissues.上皮组织中急性原发性病毒感染和免疫反应的多尺度、多细胞、时空建模的模块化框架及其在药物治疗时机和有效性方面的应用:上皮组织中病毒感染的多尺度模型
bioRxiv. 2020 Sep 26:2020.04.27.064139. doi: 10.1101/2020.04.27.064139.
6
Modelling Degradation and Replication Kinetics of the Zika Virus In Vitro Infection.模拟寨卡病毒体外感染的降解和复制动力学。
Viruses. 2020 May 15;12(5):547. doi: 10.3390/v12050547.
7
A mathematical model describing the localization and spread of influenza A virus infection within the human respiratory tract.描述甲型流感病毒在人体呼吸道内定位和传播的数学模型。
PLoS Comput Biol. 2020 Apr 13;16(4):e1007705. doi: 10.1371/journal.pcbi.1007705. eCollection 2020 Apr.
8
The rate of viral transfer between upper and lower respiratory tracts determines RSV illness duration.上、下呼吸道之间的病毒传播速率决定了呼吸道合胞病毒疾病的持续时间。
J Math Biol. 2019 Jul;79(2):467-483. doi: 10.1007/s00285-019-01364-1. Epub 2019 Apr 22.
9
Host-pathogen kinetics during influenza infection and coinfection: insights from predictive modeling.流感感染和合并感染期间的宿主-病原体动力学:预测模型的见解。
Immunol Rev. 2018 Sep;285(1):97-112. doi: 10.1111/imr.12692.
10
Influenza Virus Infection Model With Density Dependence Supports Biphasic Viral Decay.具有密度依赖性的流感病毒感染模型支持双相病毒衰减。
Front Microbiol. 2018 Jul 10;9:1554. doi: 10.3389/fmicb.2018.01554. eCollection 2018.