• 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

年龄结构对靶向治疗药物的细胞群体反应的影响。

The contribution of age structure to cell population responses to targeted therapeutics.

机构信息

UMR 7598 LJLL, BC187, Université Pierre et Marie Curie-Paris 6, 4 Place de Jussieu, F-75252 Paris Cedex 5, France.

出版信息

J Theor Biol. 2012 Oct 21;311:19-27. doi: 10.1016/j.jtbi.2012.07.001. Epub 2012 Jul 11.

DOI:10.1016/j.jtbi.2012.07.001
PMID:22796330
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3592383/
Abstract

Cells grown in culture act as a model system for analyzing the effects of anticancer compounds, which may affect cell behavior in a cell cycle position-dependent manner. Cell synchronization techniques have been generally employed to minimize the variation in cell cycle position. However, synchronization techniques are cumbersome and imprecise and the agents used to synchronize the cells potentially have other unknown effects on the cells. An alternative approach is to determine the age structure in the population and account for the cell cycle positional effects post hoc. Here we provide a formalism to use quantifiable lifespans from live cell microscopy experiments to parameterize an age-structured model of cell population response.

摘要

在培养的细胞中,作为分析抗癌化合物影响的模型系统,这些化合物可能以细胞周期位置依赖的方式影响细胞行为。通常采用细胞同步化技术来最小化细胞周期位置的变化。然而,同步化技术繁琐且不精确,并且用于同步化细胞的试剂可能对细胞产生其他未知的影响。另一种方法是确定群体中的年龄结构,并在事后考虑细胞周期位置的影响。在这里,我们提供了一种形式主义,利用活细胞显微镜实验中的可量化寿命来参数化细胞群体反应的年龄结构模型。

相似文献

1
The contribution of age structure to cell population responses to targeted therapeutics.年龄结构对靶向治疗药物的细胞群体反应的影响。
J Theor Biol. 2012 Oct 21;311:19-27. doi: 10.1016/j.jtbi.2012.07.001. Epub 2012 Jul 11.
2
Cancer cell senescence: a new frontier in drug development.癌细胞衰老:药物开发的新前沿。
Drug Discov Today. 2012 Mar;17(5-6):269-76. doi: 10.1016/j.drudis.2012.01.019. Epub 2012 Jan 31.
3
Stilbene Compounds Inhibit Tumor Growth by the Induction of Cellular Senescence and the Inhibition of Telomerase Activity.二苯乙烯类化合物通过诱导细胞衰老和抑制端粒酶活性来抑制肿瘤生长。
Int J Mol Sci. 2019 Jun 2;20(11):2716. doi: 10.3390/ijms20112716.
4
Tumor senescence as a determinant of drug response in vivo.肿瘤衰老作为体内药物反应的一个决定因素。
Drug Resist Updat. 2002 Oct;5(5):204-8. doi: 10.1016/s1368764602001103.
5
Targeting Cancer Metabolism and Cell Cycle by Plant-Derived Compounds.靶向植物源化合物的癌症代谢和细胞周期。
Adv Exp Med Biol. 2020;1247:125-134. doi: 10.1007/5584_2019_449.
6
Too big not to fail: emerging evidence for size-induced senescence.太大而无法避免失败:大小诱导衰老的新证据。
FEBS J. 2024 Jun;291(11):2291-2305. doi: 10.1111/febs.16983. Epub 2023 Nov 20.
7
Induction of senescence in cancer cells by 5'-Aza-2'-deoxycytidine: Bioinformatics and experimental insights to its targets.5'-氮杂-2'-脱氧胞苷诱导癌细胞衰老:对其靶点的生物信息学及实验见解
Comput Biol Chem. 2017 Oct;70:49-55. doi: 10.1016/j.compbiolchem.2017.08.003. Epub 2017 Aug 2.
8
The impact of cellular senescence in cancer therapy: is it true or not?细胞衰老对癌症治疗的影响:这是真的吗?
Acta Pharmacol Sin. 2011 Oct;32(10):1199-207. doi: 10.1038/aps.2011.108. Epub 2011 Sep 12.
9
Roles of TP53 in determining therapeutic sensitivity, growth, cellular senescence, invasion and metastasis.TP53在决定治疗敏感性、生长、细胞衰老、侵袭和转移方面的作用。
Adv Biol Regul. 2017 Jan;63:32-48. doi: 10.1016/j.jbior.2016.10.001. Epub 2016 Oct 6.
10
4,5-Diphenyl-2-methyl picolinate induces cellular senescence by accumulating DNA damage and activating associated signaling pathways in gastric cancer.4,5-二苯基-2-甲基吡啶酸酯通过在胃癌中积累 DNA 损伤并激活相关信号通路诱导细胞衰老。
Life Sci. 2019 Dec 1;238:116973. doi: 10.1016/j.lfs.2019.116973. Epub 2019 Oct 19.

引用本文的文献

1
Characterising the Behaviour of a Structured PDE Model of the Cell Cycle in Contrast to a Corresponding ODE System.对比相应的常微分方程系统,刻画细胞周期结构化偏微分方程模型的行为。
Bull Math Biol. 2025 Jun 8;87(7):93. doi: 10.1007/s11538-025-01472-8.
2
Stability analysis of a multiscale model of cell cycle dynamics coupled with quiescent and proliferating cell populations.细胞周期动力学多尺度模型与静息和增殖细胞群体耦合的稳定性分析。
PLoS One. 2023 Jan 20;18(1):e0280621. doi: 10.1371/journal.pone.0280621. eCollection 2023.
3
A framework for macroscopic phase-resetting curves for generalised spiking neural networks.

本文引用的文献

1
Fractional proliferation: a method to deconvolve cell population dynamics from single-cell data.分数增殖:一种从单细胞数据中推断细胞群体动态的方法。
Nat Methods. 2012 Sep;9(9):923-8. doi: 10.1038/nmeth.2138. Epub 2012 Aug 12.
2
Modelling the effects of cell-cycle heterogeneity on the response of a solid tumour to chemotherapy: biological insights from a hybrid multiscale cellular automaton model.建立细胞周期异质性对实体瘤化疗反应影响的模型:来自混合多尺度细胞自动机模型的生物学见解。
J Theor Biol. 2012 Sep 7;308:1-19. doi: 10.1016/j.jtbi.2012.05.015. Epub 2012 May 29.
3
A new model for the estimation of cell proliferation dynamics using CFSE data.
广义尖峰神经网络的宏观相位重置曲线框架。
PLoS Comput Biol. 2022 Aug 1;18(8):e1010363. doi: 10.1371/journal.pcbi.1010363. eCollection 2022 Aug.
4
Evolution of cancer stem cell lineage involving feedback regulation.涉及反馈调节的癌症干细胞谱系进化。
PLoS One. 2021 May 20;16(5):e0251481. doi: 10.1371/journal.pone.0251481. eCollection 2021.
5
A computational model of feedback-mediated hematopoietic stem cell differentiation in vitro.体外反馈介导的造血干细胞分化的计算模型。
PLoS One. 2019 Mar 1;14(3):e0212502. doi: 10.1371/journal.pone.0212502. eCollection 2019.
6
A drift-diffusion checkpoint model predicts a highly variable and growth-factor-sensitive portion of the cell cycle G1 phase.一个漂移扩散检查点模型预测了细胞周期G1期高度可变且对生长因子敏感的部分。
PLoS One. 2018 Feb 12;13(2):e0192087. doi: 10.1371/journal.pone.0192087. eCollection 2018.
7
Structured models of cell migration incorporating molecular binding processes.纳入分子结合过程的细胞迁移结构化模型。
J Math Biol. 2017 Dec;75(6-7):1517-1561. doi: 10.1007/s00285-017-1120-y. Epub 2017 Apr 12.
8
Stochastic multi-scale models of competition within heterogeneous cellular populations: Simulation methods and mean-field analysis.异质细胞群体内竞争的随机多尺度模型:模拟方法与平均场分析。
J Theor Biol. 2016 Oct 21;407:161-183. doi: 10.1016/j.jtbi.2016.07.028. Epub 2016 Jul 22.
利用 CFSE 数据估计细胞增殖动力学的新模型。
J Immunol Methods. 2011 Oct 28;373(1-2):143-60. doi: 10.1016/j.jim.2011.08.014. Epub 2011 Aug 24.
4
On the calibration of a size-structured population model from experimental data.基于实验数据的大小结构种群模型校准
Acta Biotheor. 2010 Dec;58(4):405-13. doi: 10.1007/s10441-010-9114-9. Epub 2010 Jul 30.
5
Exponentially modified Gaussian (EMG) relevance to distributions related to cell proliferation and differentiation.指数修正高斯(EMG)与细胞增殖和分化相关分布的相关性。
J Theor Biol. 2010 Jan 21;262(2):257-66. doi: 10.1016/j.jtbi.2009.10.005. Epub 2009 Oct 13.
6
Invasion emerges from cancer cell adaptation to competitive microenvironments: quantitative predictions from multiscale mathematical models.侵袭源于癌细胞对竞争性微环境的适应:多尺度数学模型的定量预测。
Semin Cancer Biol. 2008 Oct;18(5):338-48. doi: 10.1016/j.semcancer.2008.03.018. Epub 2008 Apr 1.
7
Modeling the influence of the E-cadherin-beta-catenin pathway in cancer cell invasion: a multiscale approach.模拟E-钙黏蛋白-β-连环蛋白通路在癌细胞侵袭中的影响:一种多尺度方法。
Biophys J. 2008 Jul;95(1):155-65. doi: 10.1529/biophysj.107.114678. Epub 2008 Mar 13.
8
A hybrid cellular automaton model of clonal evolution in cancer: the emergence of the glycolytic phenotype.癌症克隆进化的混合细胞自动机模型:糖酵解表型的出现。
J Theor Biol. 2008 Feb 21;250(4):705-22. doi: 10.1016/j.jtbi.2007.10.038. Epub 2007 Nov 4.
9
An age-and-cyclin-structured cell population model for healthy and tumoral tissues.一种用于健康组织和肿瘤组织的年龄与细胞周期蛋白结构的细胞群体模型。
J Math Biol. 2008 Jul;57(1):91-110. doi: 10.1007/s00285-007-0147-x. Epub 2007 Dec 7.
10
Modeling T cell proliferation and death in vitro based on labeling data: generalizations of the Smith-Martin cell cycle model.基于标记数据的体外T细胞增殖与死亡建模:史密斯 - 马丁细胞周期模型的推广
Bull Math Biol. 2008 Jan;70(1):21-44. doi: 10.1007/s11538-007-9239-4. Epub 2007 Aug 15.