论文标题
部分可观测时空混沌系统的无模型预测
An Onsager-Machlup approach to the most probable transition pathway for a genetic regulatory network
论文作者
论文摘要
我们研究了描述枯草芽孢杆菌能力发展的基因表达动力学的定量网络。首先，我们引入了一种onsager-Machlup方法，以量化可激发和可激发动态的最可能的过渡途径。然后，我们采用机器学习方法来通过Euler-Lagrangian方程计算最可能的过渡途径。最后，我们分析噪声强度如何影响过渡现象。
We investigate a quantitative network of gene expression dynamics describing the competence development in Bacillus subtilis. First, we introduce an Onsager-Machlup approach to quantify the most probable transition pathway for both excitable and bistable dynamics. Then, we apply a machine learning method to calculate the most probable transition pathway via the Euler-Lagrangian equation. Finally, we analyze how the noise intensity affects the transition phenomena.
