生物化学反应网络中反应事件的查询-响应因果分析
1ZJU-UIUC Institute, 314400, Haining, Zhejiang, China.
Computational biology and chemistry
|December 1, 2023
概括
这项研究引入了一种分析生化反应事件序列的新方法,通过评估经验概率来确定因果关系. 这种方法有助于理解生物网络的动态,并自动化研究管道.
科学领域:
- 系统生物学 系统生物学
- 计算生物学 计算生物学
- 生物物理学的生物物理.
背景情况:
- 生物化学反应网络的静态动力学通常使用化学主方程 (CME) 建模.
- 分析通常侧重于依赖时间的物种数量,忽视了反应事件的时间序列.
- 基于规则的建模和无网络模拟提供了模拟生化反应网络 (BRNs) 的替代方法.
研究的目的:
- 从随机模拟中开发一种有效的方法来识别因果和反因果相关的反应事件子序列.
- 通过分析反应事件时间序列,揭示BRNs中的因果动态和短期决定性行为.
- 用距离指标将时间排序的反应事件序列转换为集或多集,用于使用距离指标进行等价性分析.
主要方法:
- 统计评估反应事件的序列产生的BRNs的无网络模拟.
- 利用经验概率来确定有条件的几乎确定的或不确定的反应子序列作为因果关系.
- 实施一个计算效率高的查询-响应机制来识别因果相关的反应子序列.
- 修改NFsim模拟器以记录反应事件痕迹,并使用Python和Matlab脚本分析时间序列数据.
主要成果:
- 成功开发并实施了一种用于识别反应事件序列中的因果关系的新方法.
- 该方法在七个数值实验中对五个遗传网络模型进行了评估,证明了其适用性.
- 分析揭示了通过自动化数据生成和处理管道提高研究生产率的机会.
结论:
- 拟议的方法通过分析反应事件序列,有效地识别BRNs中的因果动态.
- 将事件序列转换为集合,并使用距离指标,可以定义反应事件之间的等价值.
- 数据生成和分析管道的自动化显著提高了系统生物学研究效率.
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