Boseung Choi

The paper develops a Bayesian MCMC method to infer gene regulatory network parameters from partial observations, demonstrating its ability to estimate delays and kinetic parameters in a two-step activation model.

The paper introduces a hierarchical Bayesian framework to infer variability in gene regulatory processes with delays using both simulated and experimental data, showing improved parameter estimates.

The paper introduces a Bayesian framework to infer reaction rates and delay distributions in biochemical networks using single-cell data, showing robust parameter estimation with limited measurements.

This paper introduces a Bayesian method for estimating enzyme kinetic parameters with improved accuracy and efficiency compared to the traditional Michaelis-Menten approach.