Department of Engineering Science & Mechanics, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA.
Cell Prolif. 2010 Dec;43(6):542-52. doi: 10.1111/j.1365-2184.2010.00703.x.
Mathematical models are useful for studying vascular and avascular tumours, because these allow for more logical experimental design and provide valuable insights into the underlying mechanisms of their growth and development. The processes of avascular tumour growth and the development of capillary networks through tumour-induced angiogenesis have already been extensively investigated, albeit separately. Despite the clinical significance of vascular tumours, few studies have combined these approaches to develop a single comprehensive growth and development model.
We develop a continuum-based mathematical model of vascular tumour growth. In the model, angiogenesis is initiated through the release of angiogenic growth factors (AGFs) by cells in the hypoxic regions of the tumour. The nutrient concentration within the tumour reflects the influence of capillary growth and invasion induced by AGF.
Parametric and sensitivity studies were performed to evaluate the influence of different model parameters on tumour growth and to identify the parameters with the most influence, which include the rates of proliferation, apoptosis and necrosis, as well as the diffusion of sprout tips and the size of the region affected by angiogenesis. An optimization was performed for values of the model parameters that resulted in the best agreement with published experimental data. The resulting model solution matched the experimental data with a high degree of correlation (r = 0.85).
数学模型在研究血管性和乏血管性肿瘤方面非常有用,因为这些模型可以进行更符合逻辑的实验设计,并深入了解肿瘤生长和发育的潜在机制。尽管血管性肿瘤具有重要的临床意义,但目前很少有研究将这些方法结合起来,以建立一个单一的全面生长和发育模型。
我们开发了一种基于连续统的血管性肿瘤生长数学模型。在该模型中,血管生成是通过肿瘤缺氧区域细胞释放血管生成生长因子(angiogenic growth factors,AGFs)引发的。肿瘤内的营养浓度反映了血管生成和血管生成诱导的毛细血管生长和侵袭的影响。
进行了参数和敏感性研究,以评估不同模型参数对肿瘤生长的影响,并确定对肿瘤生长影响最大的参数,包括增殖、凋亡和坏死的速率,以及芽尖的扩散和血管生成影响区域的大小。针对使模型与已发表的实验数据具有最佳一致性的参数值进行了优化。所得模型解与实验数据具有高度相关性(r = 0.85)。