一个混合效应的随机模型揭示了基因疗法安全性研究中的克隆主导地位
Luca Del Core1,2, Danilo Pellin3, Ernst C Wit4,5
1Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Groningen, The Netherlands. luca.delcore@nottingham.ac.uk.
BMC bioinformatics
|June 2, 2023
概括
这项研究引入了一个新的统计框架,用于使用克隆追踪数据分析基因治疗中的克隆主导地位. 该方法准确地识别异常细胞扩张,增强了对患者的安全监测.
科学领域:
- 数学生物学 数学生物学
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
背景情况:
- 血液形成的数学模型对于理解异常细胞扩张 (克隆主导) 和指导基因疗法安全至关重要.
- 高通量克隆追踪量化了基因治疗后单个造血干细胞祖先的细胞.
- 克隆跟踪数据可以对体内克隆动态的随机微分方程进行校准.
研究的目的:
- 开发一种随机效应的随机框架,用于使用高维的克隆跟踪数据调查克隆统治.
- 在体内建模克隆群体动态和层次关系.
- 为基因疗法安全性分析提供计算工具.
主要方法:
- 组合的随机反应网络和混合效应的通用线性模型.
- 使用克拉默斯-莫亚尔近似来进行细胞动态局部线性近似.
- 采用最大概率方法和定制期望最大化算法用于参数推断和模型校准.
主要成果:
- 开发了一个随机效应的随机框架,以从克隆追踪数据中检测克隆主导地位.
- 扩展模型包含随机效应,以解释适应性中的克隆异质性.
- 引入了R包RestoreNet,用于公共访问和框架的应用.
结论:
- 与现有的最先进的方法相比,提出的方法显示出更高的性能.
- 在体内研究中的应用揭示了克隆主导的关键动态.
- 该工具为生物研究中的基因疗法安全性评估提供了有价值的统计支持.
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