Oliver J. Maclaren

This paper applies the Profile-Wise Analysis workflow to non-identifiable mathematical models in population growth to improve prediction and parameter estimation.

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

The paper introduces a workflow called Profile-Wise Analysis for identifiability analysis, parameter estimation, and prediction using mechanistic mathematical models, demonstrating its application with ODE models.

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

The paper develops a hierarchical Bayesian framework to model spatiotemporal dynamics of intestinal epithelium proliferation and migration under different conditions, concluding a proliferation-driven model is sufficient for most time-scales.