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对瘤生长的经典,分数和多尺度物流模型的校准和不确定性量化分析

Nikolaos M Dimitriou¹, Ece Demirag², Katerina Strati²

¹Department of Bioengineering, McGill University, Montreal, H3A 0E9, QC, Canada.

Computer methods and programs in biomedicine

|November 17, 2023

概括

瘤生长的数学模型从多尺度数据集成中受益. 结合基因表达,如OCT4,可以改善定量分析,特别是高剂量治疗,提高模型性能.

科学领域:

数学生物学数学生物学
计算瘤学计算瘤学
系统生物学系统生物学

背景情况:

瘤生长模型的验证受到稀缺的实验数据的限制,阻碍了定量研究.
多尺度方法整合了多样化的数据 (例如,细胞计数,基因表达) 以改善瘤生长分析.
研究基因表达对化学敏感性和细胞死亡的影响对于治疗建模至关重要.

研究的目的:

评估数学模型 (逻辑,分数,多尺度) 用于在不同治疗条件下量化体外瘤生长.
评估特定基因对细胞死亡率和化学敏感性的影响.
通过集成的基因表达数据来确定多尺度模型在捕获瘤动态方面的性能.

主要方法:

通过将基因表达特征与细胞死亡率联系起来,开发了多层次的后勤生长模型.
采用贝叶斯推理,概率最大化和不确定性量化,用于严格的模型评估.
进行识别性分析,以评估模型的稳定性与已知的基因表达数据.

主要成果:

经典的单细胞种群模型 (SCPM) 最适合未治疗和低剂量治疗数据.
一个包含OCT4表达的多尺度模型 (Sym-SCPM) 在高剂量治疗场景中表现出色.
可识别性分析证实了多尺度模型的结构和实际可识别性与已知的OCT4配置文件.

结论:

整合多尺度测量显著提高瘤生长模型的性能,特别是在高剂量治疗干预下.
基因表达数据,特别是OCT4,在建模细胞死亡率和治疗反应方面发挥着关键作用.
多尺度建模为研究瘤生长动态和治疗效应提供了更定量和更准确的方法.

关键词:

贝叶斯的推理贝叶斯的推理分数式的分数式.可能性优化概率优化.后勤模型后勤模型多层次的多层次的瘤的生长瘤的生长不确定性量化不确定性的量化.

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