Ryan J. Murphy

A multilevel hierarchical framework for quantification of experimental heterogeneity

This paper introduces a Bayesian hierarchical framework to quantify both inter- and intra-experimental heterogeneity in dynamical processes using differential equations, demonstrating its robustness and flexibility.

Quantifying biological heterogeneity in nano-engineered particle-cell interaction experiments

This study develops a mathematical model to quantify heterogeneity in particle-cell interaction experiments and identifies optimal experimental time points for accurate parameter estimation.

Modelling count data with partial differential equation models in biology

The paper compares Gaussian and binomial measurement error models for PDE-based biological population dynamics, finding the binomial model provides biologically plausible predictions.

Computationally efficient framework for diagnosing, understanding, and predicting biphasic population growth

The paper develops a computationally efficient framework for diagnosing, understanding, and predicting biphasic population growth using profile likelihood analysis.