评估生物网络动态:比较数值模拟与参数空间的分析分解
Kishore Hari1, William Duncan2, Mohammed Adil Ibrahim3
1Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore, 560012, India.
NPJ systems biology and applications
|July 3, 2023
概括
通过比较RACIPE和DSGRN这两种计算方法,我们发现在预测基因调节网络 (GRN) 动态方面存在强烈一致,尽管参数假设不同. 这验证了DSGRN的有效性.
科学领域:
- 计算生物学 计算生物学
- 系统生物学 系统生物学
- 生物信息学是一种生物信息学.
背景情况:
- 基因调控网络 (GRNs) 呈现出复杂的新兴动态.
- 准确的GRNs数学建模受到参数不确定性的阻碍.
- 实验性确定GRN参数仍然具有挑战性.
研究的目的:
- 为了比较两种不同的GRN动态计算方法的预测能力:RACIPE和DSGRN.
- 评估参数采样 (RACIPE) 和组合近似 (DSGRN) 方法之间的一致性.
- 为了评估DSGRN预测在不同参数范围的稳定性.
主要方法:
- 使用RACIPE (随机电路扰动) 进行参数采样和集体统计.
- 使用DSGRN (由监管网络生成的动态签名) 来对ODE模型进行组合性近似.
- 在四个代表性的2和3节点GRN模型上进行了验证的预测,这些模型与细胞决策相关.
主要成果:
- 对于测试的GRN模型,RACIPE模拟和DSGRN预测之间的高度一致性被证明.
- 展示了DSGRN的预测准确性,即使它对高希尔系数的假设与RACIPE更广泛的范围 (1-6) 不一样.
- 证实DSGRN的参数域,由参数不等式定义,准确地预测生物相关参数范围内的ODE模型动态.
结论:
- RACIPE和DSGRN为分析参数不确定性下的GRN动态提供互补和高度一致的方法.
- DSGRN提供了一个强大的框架,可以通过广泛的生物可信参数来预测GRN行为.
- 这些发现支持DSGRN的实用性,以了解由基因调节控制的细胞决策过程.
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