Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, Massachusetts, United States of America.
Department of Civil and Environmental Engineering, University of Massachusetts, Dartmouth, Massachusetts, United States of America.
PLoS Comput Biol. 2022 Mar 16;18(3):e1009953. doi: 10.1371/journal.pcbi.1009953. eCollection 2022 Mar.
The most common kind of cancer among women is breast cancer. Understanding the tumor microenvironment and the interactions between individual cells and cytokines assists us in arriving at more effective treatments. Here, we develop a data-driven mathematical model to investigate the dynamics of key cell types and cytokines involved in breast cancer development. We use time-course gene expression profiles of a mouse model to estimate the relative abundance of cells and cytokines. We then employ a least-squares optimization method to evaluate the model's parameters based on the mice data. The resulting dynamics of the cells and cytokines obtained from the optimal set of parameters exhibit a decent agreement between the data and predictions. We perform a sensitivity analysis to identify the crucial parameters of the model and then perform a local bifurcation on them. The results reveal a strong connection between adipocytes, IL6, and the cancer population, suggesting them as potential targets for therapies.
女性最常见的癌症是乳腺癌。了解肿瘤微环境以及单个细胞和细胞因子之间的相互作用有助于我们制定更有效的治疗方法。在这里，我们开发了一个数据驱动的数学模型来研究参与乳腺癌发展的关键细胞类型和细胞因子的动态。我们使用小鼠模型的时间过程基因表达谱来估计细胞和细胞因子的相对丰度。然后，我们使用最小二乘优化方法根据小鼠数据评估模型的参数。从最佳参数集中获得的细胞和细胞因子的动力学与数据和预测之间具有良好的一致性。我们进行敏感性分析以确定模型的关键参数，然后对其进行局部分岔分析。结果表明脂肪细胞、IL6 和癌细胞之间存在很强的联系，提示它们可能成为治疗的潜在靶点。
