An Onsager–Machlup approach to the most probable transition pathway for a genetic regulatory network

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.