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A PROGNOSTIC IMMUNOTHERAPY MODEL FOR 4T1 BREAST CANCER WITH COMBINED CYCLOPHOSPHAMIDE AND TLR AGONIST
Journal of Biological Systems ( IF 1.6 ) Pub Date : 2020-02-18 , DOI: 10.1142/s0218339020500035
DANHUA HE 1 , WEINAN XU 2 , XUEFANG LI 3, 4 , JIAN-XIN XU 5
Affiliation  

Based on experimental results of a mouse model provided in the literature, we develop a mathematical model by using system biology approach, aiming to investigate immunotherapy for 4T1 breast cancer. It is worth to mention that only 4 types of cells (tumor cells, CD8[Formula: see text] T cells, regular T cells (Tregs), and tumoricidal myeloid CD11b[Formula: see text]Gr1dim cells) are quantitatively measured in experiments, which make the immunotherapy modelling more difficult since only limited system knowledge is available. To overcome the difficulty, the mathematical model is proposed by employing Evolutionary Computation to optimize the system parameters. Furthermore, with the mathematical model, analysis can be conducted to capture the inherent properties of the model, such as the number and stability of equilibria, and parameter sensitivity analysis, which disclose the nature of 4T1 breast cancer from a system biological perspective. Not limited to replication of experimental results, we further show that the mathematical model is in fact a prognostic immunotherapy model that can predict treatment outcomes of various cases; for instance, different combinations of drug delivery schedules. By virtue of computational convenience, it is relatively easy to intensively investigate most of the treatments that are impossible for animal models or clinical trials. In other words, a mathematical model based on system biology can provide meaningful reference when exploiting more effective treatment protocols.

中文翻译:

结合环磷酰胺和 TLR 激动剂的 4T1 乳腺癌预后免疫治疗模型

基于文献中提供的小鼠模型的实验结果,我们利用系统生物学方法建立了一个数学模型,旨在研究 4T1 乳腺癌的免疫治疗。值得一提的是,实验中仅定量测量了4种细胞(肿瘤细胞、CD8[公式:见文]T细胞、常规T细胞(Tregs)、杀肿瘤髓系CD11b[公式:见文]Gr1dim细胞) ,这使得免疫疗法建模更加困难,因为只有有限的系统知识可用。为了克服这一困难,提出了采用进化计算优化系统参数的数学模型。此外,通过数学模型,可以进行分析以捕捉模型的固有属性,例如平衡的数量和稳定性,和参数敏感性分析,从系统生物学角度揭示4T1乳腺癌的性质。不仅限于实验结果的复制,我们进一步表明该数学模型实际上是一种预后免疫治疗模型,可以预测各种病例的治疗结果;例如,药物输送时间表的不同组合。由于计算方便,对动物模型或临床试验不可能进行的大多数治疗进行深入研究是相对容易的。换句话说,基于系统生物学的数学模型可以在开发更有效的治疗方案时提供有意义的参考。我们进一步表明,该数学模型实际上是一种预后免疫治疗模型,可以预测各种病例的治疗结果;例如,药物输送时间表的不同组合。由于计算方便,对动物模型或临床试验不可能进行的大多数治疗进行深入研究是相对容易的。换句话说,基于系统生物学的数学模型可以在开发更有效的治疗方案时提供有意义的参考。我们进一步表明,该数学模型实际上是一种预后免疫治疗模型,可以预测各种病例的治疗结果;例如,药物输送时间表的不同组合。由于计算方便,对动物模型或临床试验不可能进行的大多数治疗进行深入研究是相对容易的。换句话说,基于系统生物学的数学模型可以在开发更有效的治疗方案时提供有意义的参考。对动物模型或临床试验不可能的大多数治疗方法进行深入研究相对容易。换句话说,基于系统生物学的数学模型可以在开发更有效的治疗方案时提供有意义的参考。对动物模型或临床试验不可能的大多数治疗方法进行深入研究相对容易。换句话说,基于系统生物学的数学模型可以在开发更有效的治疗方案时提供有意义的参考。
更新日期:2020-02-18
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