确定基于进化策略的有效协议,用于在测量噪声的影响下发现反应动力学参数
Hock Chuan Yeo1, Varsheni Vijay2, Kumar Selvarajoo3,4,5,6
1Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, Matrix #07-01, Singapore, 138761, Republic of Singapore.
BMC biology
|October 14, 2024
概括
我们比较了在系统生物学中恢复反应参数的进化算法. 对于特定的动力学,CMAES和G3PCX显示出效率,而SRES则提供了多功能性,有助于在噪音下进行预测建模.
科学领域:
- 系统生物学 系统生物学
- 计算生物学 计算生物学
- 生物化学运动学 生物化学运动学
背景情况:
- 准确的反应参数恢复对于系统生物学中的预测建模至关重要.
- 在不同的动力学和噪声水平下,优化算法的性能还未得到充分研究.
研究的目的:
- 为了比较5种进化算法 (CMAES,DE,SRES,ISRES,G3PCX) 估计反应参数的有效性.
- 为了评估四种动力学配方 (GMA,迈凯利斯-门,线性-逻辑,方便动力学) 和不同的测量噪声水平的算法性能.
主要方法:
- 使用四种动力学配方对人工路径进行模拟反应.
- 应用了五种进化算法来优化动力参数超空间中的客观函数.
- 基于准确性,计算成本和对测量噪声的弹性来评估算法性能.
主要成果:
- 由于性能差,DE算法被丢弃.
- 对于低噪音的GMA和线性对数动力学,CMAES在计算上是高效的.
- 对于高噪音的GMA动力学,SRES和ISRES显示出可靠性,但计算成本更高.
- G3PCX对迈凯利斯-门动力学有效,节省了大量的成本.
- 在GMA,Michaelis-Menten和线性-对数动力学中,SRES展示了多功能性.
- 方便动力学参数无法通过任何算法确定.
结论:
- 确定了一个用于预测反应参数的协议,特别是在测量噪声下.
- 这项工作有助于推进系统生物学中的预测建模能力.
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