An Onsager-Machlup approach to the most probable transition pathway for a genetic regulatory network
arXiv:2203.00864 · doi:10.1063/5.0088397
Abstract
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.