Hyukpyo Hong

The paper develops Density-PINN to infer transduction-time distributions from single-cell gene expression data and finds that heterogeneity in response intensity is reduced when multiple signaling pathways regulate the response.

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 shows that non-elementary propensities can accurately model stochastic dynamics of reversible bindings but fail in certain cases, and introduces a new framework with an open-source tool to address this.

The paper develops a computational package to analyze sleep-wake patterns and shows that aligning them with circadian rhythm reduces daytime sleepiness, even with less total sleep.

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 develops a method to derive analytic stationary distributions for biochemical reaction networks by transforming them to be complex balanced, and introduces a computational tool for this purpose.