利用多尺度数学模型探讨EMT在卵巢癌进展中的作用。

IF 3.5 2区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Samuel Oliver, Michael Williams, Mohit Kumar Jolly, Deyarina Gonzalez, Gibin Powathil
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引用次数: 0

摘要

上皮间质转化(Epithelial-to-mesenchymal transition, EMT)在癌症肿瘤的进展中起着关键作用,显著降低了治疗的成功率。EMT发生时,细胞经历表型变化,导致增强的耐药性,更高的细胞可塑性,并增加转移能力。在这里,我们使用了一个基于3D代理的多尺度建模框架,使用PhysiCell来探索EMT在两种细胞系OVCAR-3和SKOV-3中随时间的作用。这种方法使我们能够研究卵巢癌的时空进展和微环境条件的影响。OVCAR-3和SKOV-3细胞系具有高度不同的肿瘤布局,允许广泛不同的肿瘤动力学和形态学进行测试和研究。除了对模型进行敏感性分析外,模拟结果还捕获了肿瘤生长和发育过程中的生物学观察和趋势,从而有助于进一步了解OVCAR-3和SKOV-3细胞系动力学。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exploring the role of EMT in ovarian cancer progression using a multiscale mathematical model.

Epithelial-to-mesenchymal transition (EMT) plays a key role in the progression of cancer tumours, significantly reducing the success of treatment. EMT occurs when a cell undergoes phenotypical changes, resulting in enhanced drug resistance, higher cell plasticity, and increased metastatic abilities. Here, we employ a 3D agent-based multiscale modelling framework using PhysiCell to explore the role of EMT over time in two cell lines, OVCAR-3 and SKOV-3. This approach allows us to investigate the spatiotemporal progression of ovarian cancer and the impacts of the conditions in the microenvironment. OVCAR-3 and SKOV-3 cell lines possess highly contrasting tumour layouts, allowing a wide range of different tumour dynamics and morphologies to be tested and studied. Along with performing sensitivity analysis on the model, simulation results capture the biological observations and trends seen in tumour growth and development, thus helping to obtain further insights into OVCAR-3 and SKOV-3 cell line dynamics.

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来源期刊
NPJ Systems Biology and Applications
NPJ Systems Biology and Applications Mathematics-Applied Mathematics
CiteScore
5.80
自引率
0.00%
发文量
46
审稿时长
8 weeks
期刊介绍: npj Systems Biology and Applications is an online Open Access journal dedicated to publishing the premier research that takes a systems-oriented approach. The journal aims to provide a forum for the presentation of articles that help define this nascent field, as well as those that apply the advances to wider fields. We encourage studies that integrate, or aid the integration of, data, analyses and insight from molecules to organisms and broader systems. Important areas of interest include not only fundamental biological systems and drug discovery, but also applications to health, medical practice and implementation, big data, biotechnology, food science, human behaviour, broader biological systems and industrial applications of systems biology. We encourage all approaches, including network biology, application of control theory to biological systems, computational modelling and analysis, comprehensive and/or high-content measurements, theoretical, analytical and computational studies of system-level properties of biological systems and computational/software/data platforms enabling such studies.
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