Purpose: In recent years, mathematical models have become instrumental in cancer research, offering insights into tumor growth dynamics, and guiding the development of pharmacological strategies. These models, encompassing diverse biological and physical processes, are increasingly used in clinical settings, showing remarkable predictive precision for individual patient outcomes and therapeutic responses. Methods: Motivated by these advancements, our study introduces an innovative in silico model for simulating tumor growth and invasiveness. The automated hybrid cell emulates critical tumor cell characteristics, including rapid proliferation, heightened motility, reduced cell adhesion, and increased responsiveness to chemotactic signals. This model explores the potential evolution of 3D tumor spheroids by manipulating biological parameters and microenvironment factors, focusing on nutrient availability. Results: Our comprehensive global and local sensitivity analysis reveals that tumor growth primarily depends on cell duplication speed and cell-to-cell adhesion, rather than external chemical gradients. Conversely, tumor invasiveness is predominantly driven by chemotaxis. These insights illuminate tumor development mechanisms, providing vital guidance for effective strategies against tumor progression. Our proposed model is a valuable tool for advancing cancer biology research and exploring potential therapeutic interventions.
目的：近年来，数学模型在癌症研究中发挥着关键作用，为肿瘤生长动力学提供了深入见解，并指导了药物策略的开发。这些模型涵盖了多种生物和物理过程，在临床环境中应用日益广泛，对个体患者的预后和治疗反应展现出显著的预测精度。方法：基于这些进展，本研究提出了一种创新的计算机模型，用于模拟肿瘤的生长和侵袭性。该自动化混合细胞模型模拟了肿瘤细胞的关键特征，包括快速增殖、增强的运动性、降低的细胞黏附性以及对趋化信号增强的响应能力。该模型通过调控生物参数和微环境因素（重点关注营养可用性），探索了三维肿瘤球体潜在的演化过程。结果：我们全面的全局和局部敏感性分析表明，肿瘤生长主要取决于细胞复制速度和细胞间黏附性，而非外部化学梯度。相反，肿瘤侵袭性主要由趋化作用驱动。这些发现阐明了肿瘤发展的机制，为制定有效抑制肿瘤进展的策略提供了重要指导。我们提出的模型是推进癌症生物学研究和探索潜在治疗干预手段的有力工具。
Hybrid Cellular Automata Modeling Reveals the Effects of Glucose Gradients on Tumour Spheroid Growth
肿瘤(癌症)患者之家