生物网络目标控制的动态方法
Thomas Parmer1, Filippo Radicchi1
1Center for Complex Networks and Systems Research, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN 47408, USA.
Royal Society open science
|November 3, 2023
概括
对布尔网络进行比较的计算方法表明,图形理论和平均场方法在估计节点影响方面提供了可比的性能,但在精度和回忆方面存在权衡.
科学领域:
- 系统生物学 系统生物学
- 计算生物学 计算生物学
- 网络科学 网络科学
背景情况:
- 估计布尔网络中的节点影响对于预测和控制系统动态至关重要,特别是在生物信号和调节方面.
- 由于系统配置的指数式增长,精确的估计在计算上是不可行的.
- 现有的可扩展方法包括图形理论和平均场方法.
研究的目的:
- 系统地比较最先进的图形理论和平均场方法的性能,以估计布尔网络中的节点影响.
- 在现实世界的基因调节网络中对这些方法进行评估.
主要方法:
- 图形理论和平均场方法的系统性能比较.
- 对大量现实世界基因调节网络的评估.
- 基于网络连接的精度,回忆和计算速度的权衡分析.
主要成果:
- 在评估的方法中观察到可比的性能.
- 所有的方法都倾向于低估地面真理的影响.
- 与图形理论方法相比,平均场方法显示出更高的回忆力,但精度较低.
- 图形理论方法在稀疏网络上更快,而平均场方法在密集网络上更快.
结论:
- 图形理论和平均场方法之间的选择取决于特定应用程序对回忆与精度的要求以及网络的连接性.
- 这两类方法都为布尔网络动态提供了有价值的,尽管近似的见解.
- 了解这些性能权衡对于选择分析基因调节网络最合适的方法至关重要.
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