Suppr超能文献

密度依赖的神经胶质瘤侵袭静止:迁移/增殖二分法的简单反应扩散模型中的不稳定性。

Density-dependent quiescence in glioma invasion: instability in a simple reaction-diffusion model for the migration/proliferation dichotomy.

机构信息

Department of Mathematics, University of California at Irvine, Irvine, CA 92697, USA.

出版信息

J Biol Dyn. 2012;6 Suppl 1(0 1):54-71. doi: 10.1080/17513758.2011.590610. Epub 2011 Jun 27.

Abstract

Gliomas are very aggressive brain tumours, in which tumour cells gain the ability to penetrate the surrounding normal tissue. The invasion mechanisms of this type of tumour remain to be elucidated. Our work is motivated by the migration/proliferation dichotomy (go-or-grow) hypothesis, i.e. the antagonistic migratory and proliferating cellular behaviours in a cell population, which may play a central role in these tumours. In this paper, we formulate a simple go-or-grow model to investigate the dynamics of a population of glioma cells for which the switch from a migratory to a proliferating phenotype (and vice versa) depends on the local cell density. The model consists of two reaction-diffusion equations describing cell migration, proliferation and a phenotypic switch. We use a combination of numerical and analytical techniques to characterize the development of spatio-temporal instabilities and travelling wave solutions generated by our model. We demonstrate that the density-dependent go-or-grow mechanism can produce complex dynamics similar to those associated with tumour heterogeneity and invasion.

摘要

神经胶质瘤是非常侵袭性的脑肿瘤,其中肿瘤细胞获得了穿透周围正常组织的能力。这种肿瘤的侵袭机制仍有待阐明。我们的工作受到迁移/增殖二分法(去或生长)假说的启发,即细胞群体中拮抗的迁移和增殖细胞行为,这可能在这些肿瘤中起核心作用。在本文中,我们提出了一个简单的去或生长模型,以研究神经胶质瘤细胞群体的动力学,其中从迁移表型向增殖表型的转变(反之亦然)取决于局部细胞密度。该模型由两个描述细胞迁移、增殖和表型转变的反应扩散方程组成。我们使用数值和分析技术的组合来描述我们模型产生的时空不稳定性和传播波解的发展。我们证明,密度依赖性的去或生长机制可以产生类似于与肿瘤异质性和侵袭相关的复杂动力学。

相似文献

2
Identification of intrinsic in vitro cellular mechanisms for glioma invasion.
J Theor Biol. 2011 Oct 21;287:131-47. doi: 10.1016/j.jtbi.2011.07.012. Epub 2011 Jul 29.
4
'Go or grow': the key to the emergence of invasion in tumour progression?
Math Med Biol. 2012 Mar;29(1):49-65. doi: 10.1093/imammb/dqq011. Epub 2010 Jul 7.
6
The impact of phenotypic switching on glioblastoma growth and invasion.
PLoS Comput Biol. 2012;8(6):e1002556. doi: 10.1371/journal.pcbi.1002556. Epub 2012 Jun 14.
8
Slit2 inhibits glioma cell invasion in the brain by suppression of Cdc42 activity.
Neuro Oncol. 2009 Dec;11(6):779-89. doi: 10.1215/15228517-2008-017.
9
[Effects of FPR2 gene silencing on the proliferation, migration and invasion of human glioma U87 cells].
Zhonghua Zhong Liu Za Zhi. 2018 Sep 23;40(9):659-666. doi: 10.3760/cma.j.issn.0253-3766.2018.09.004.
10
Modelling microtube driven invasion of glioma.
J Math Biol. 2023 Nov 28;88(1):4. doi: 10.1007/s00285-023-02025-0.

引用本文的文献

1
Phenotype structuring in collective cell migration: a tutorial of mathematical models and methods.
J Math Biol. 2025 May 16;90(6):61. doi: 10.1007/s00285-025-02223-y.
2
Mathematical modeling of multicellular tumor spheroids quantifies inter-patient and intra-tumor heterogeneity.
NPJ Syst Biol Appl. 2025 Feb 15;11(1):20. doi: 10.1038/s41540-025-00492-3.
3
Evolution of phenotypic plasticity leads to tumor heterogeneity with implications for therapy.
PLoS Comput Biol. 2024 Aug 9;20(8):e1012003. doi: 10.1371/journal.pcbi.1012003. eCollection 2024 Aug.
4
Deep learning identifies heterogeneous subpopulations in breast cancer cell lines.
bioRxiv. 2024 Jul 4:2024.07.02.601576. doi: 10.1101/2024.07.02.601576.
5
Modelling microtube driven invasion of glioma.
J Math Biol. 2023 Nov 28;88(1):4. doi: 10.1007/s00285-023-02025-0.
6
Traveling wave speed and profile of a "go or grow" glioblastoma multiforme model.
Commun Nonlinear Sci Numer Simul. 2023 Apr;118. doi: 10.1016/j.cnsns.2022.107008. Epub 2022 Nov 17.
7
Modelling glioma progression, mass effect and intracranial pressure in patient anatomy.
J R Soc Interface. 2022 Mar;19(188):20210922. doi: 10.1098/rsif.2021.0922. Epub 2022 Mar 23.
8
A review of dynamical systems approaches for the detection of chaotic attractors in cancer networks.
Patterns (N Y). 2021 Apr 9;2(4):100226. doi: 10.1016/j.patter.2021.100226.
10
WHERE DID THE TUMOR START? AN INVERSE SOLVER WITH SPARSE LOCALIZATION FOR TUMOR GROWTH MODELS.
Inverse Probl. 2020 Apr;36(4). doi: 10.1088/1361-6420/ab649c. Epub 2020 Feb 26.

本文引用的文献

1
Identification of intrinsic in vitro cellular mechanisms for glioma invasion.
J Theor Biol. 2011 Oct 21;287:131-47. doi: 10.1016/j.jtbi.2011.07.012. Epub 2011 Jul 29.
2
Nonlinear modelling of cancer: bridging the gap between cells and tumours.
Nonlinearity. 2010;23(1):R1-R9. doi: 10.1088/0951-7715/23/1/r01.
3
'Go or grow': the key to the emergence of invasion in tumour progression?
Math Med Biol. 2012 Mar;29(1):49-65. doi: 10.1093/imammb/dqq011. Epub 2010 Jul 7.
4
5
Dissecting cancer through mathematics: from the cell to the animal model.
Nat Rev Cancer. 2010 Mar;10(3):221-30. doi: 10.1038/nrc2808.
6
A model of cell migration within the extracellular matrix based on a phenotypic switching mechanism.
Math Med Biol. 2010 Sep;27(3):255-81. doi: 10.1093/imammb/dqp021. Epub 2009 Nov 25.
7
Epigenetic gene expression noise and phenotypic diversification of clonal cell populations.
Differentiation. 2008 Jan;76(1):33-40. doi: 10.1111/j.1432-0436.2007.00219.x. Epub 2007 Sep 6.
8
Migration and proliferation dichotomy in tumor-cell invasion.
Phys Rev Lett. 2007 Mar 16;98(11):118101. doi: 10.1103/PhysRevLett.98.118101. Epub 2007 Mar 12.
9
A mathematical model of glioblastoma tumor spheroid invasion in a three-dimensional in vitro experiment.
Biophys J. 2007 Jan 1;92(1):356-65. doi: 10.1529/biophysj.106.093468. Epub 2006 Oct 13.
10
Dynamics and pattern formation in invasive tumor growth.
Phys Rev Lett. 2006 May 12;96(18):188103. doi: 10.1103/PhysRevLett.96.188103. Epub 2006 May 11.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验