Zachary Fox

The paper evaluates prediction variability in PBPK models under deep uncertainty, showing significant variation in PK statistics and model disagreement for certain chemical compounds.

This paper proposes a hybrid computational pipeline combining mechanistic models and machine learning to design single-color multiplexed Nascent Chain Tracking experiments capable of resolving multiple mRNA species.

MicroMator enables reactive microscopy experiments by integrating real-time data with machine learning for single-cell control and recombination.

The paper uses live single-cell imaging and computational modeling to show that diverse kinetic stimulations improve prediction of signaling dynamics by better constraining model parameters.

This paper develops and validates an FSP-FIM method to optimize single-cell experiments for parameter estimation in yeast stress response models.

The paper develops a computational model to simulate single-RNA translation dynamics and evaluates experimental assays for estimating elongation kinetics.

This paper introduces two Bayesian inference methods for stochastic gene expression parameters using multifidelity models, showing significant computational speedup compared to full FSP approaches.

This paper introduces a method to optimize single-cell experiments by estimating information using a finite state projection based Fisher information matrix, without assuming distribution shapes or requiring precise higher-order moments.

The paper develops a computational approach to model gene regulation by addressing distributional challenges in single-cell mRNA data, leading to predictive insights into transcription mechanisms.