Kaan Öcal

The paper develops computational algorithms to accurately estimate bacterial population growth rates using single-cell models and applies them to study antibiotic response heterogeneity and chemotaxis effects.

The paper compares deterministic and stochastic models of cellular event timing, showing significant differences in predicted trigger times under certain conditions.

The paper introduces a Bayesian framework to model single-cell RNA sequencing data, accounting for extrinsic noise and cellular heterogeneity, showing cell-specific effects alter gene expression dynamics.

This paper uses neural networks to approximate solutions of the Chemical Master Equation, enabling faster stochastic simulation and inference.

The paper introduces a synthetic model-based approach for uncertainty quantification in stochastic gene expression models, showing improved performance over existing methods.

This paper introduces a machine learning method using deep neural density estimators to perform Bayesian inference on neural dynamics models, demonstrating its application to various neural systems.