Youming Li

This paper investigates stochastic detailed balance in chemical reaction networks, showing its dependence on system volume and introducing new systems with specific potential structures.

This paper develops an effective Markovian dynamics method to solve non-Markovian gene expression models and shows it explains reduced mRNA-protein correlation and enhanced bimodality in non-exponential protein decay.

This paper investigates the Poisson representation linking discrete and continuous models of stochastic gene regulatory networks, showing it holds for unregulated genes and approximates for regulated ones in large protein number limits.

The paper derives time-dependent distributions for gene expression models with complex promoter switching, showing molecular memory affects bimodality depending on switching rates.