Non-Gaussian Noise Accelerates State Transitions in Bistable Biochemical Networks: A Stochastic Simulation Study

This work investigates how non-Gaussian noise (e.g., Lévy fluctuations) drives state transitions in bistable biochemical networks. Using stochastic simulations (Gillespie algorithm), we show that heavy-tailed noise enhances transition rates between stable states compared to Gaussian noise. Our analysis reveals a statistical coupling between noise properties and network dynamics, offering new insights into controlling cellular decision-making processes.