Reaction–diffusion models describing the movement, reproduction and death of individuals
within a population are key mathematical modelling tools with widespread applications in …

When employing mechanistic models to study biological phenomena, practical parameter
identifiability is important for making accurate predictions across wide ranges of unseen …

Equation learning methods present a promising tool to aid scientists in the modeling process
for biological data. Previous equation learning studies have demonstrated that these …

Computational models in biomedicine rely on biological and clinical assumptions. The
selection of these assumptions contributes substantially to modeling success or failure …

Partial differential equation (PDE) models are often used to study biological phenomena
involving movement-birth–death processes, including ecological population dynamics and …

Complex models of biochemical reaction systems have become increasingly common in the
systems biology literature. The complexity of such models can present a number of …

In this study, we apply the Bayesian paradigm for parameter identification to a well-studied
semi-linear reaction–diffusion system with activator-depleted reaction kinetics, posed on …

The idea that one can possibly develop computational models that predict the emergence,
growth, or decline of tumors in living tissue is enormously intriguing as such predictions …

We examine the practical identifiability of parameters in a spatio-temporal reaction–diffusion
model of a scratch assay. Experimental data involve fluorescent cell cycle labels, providing …

Understanding the mechanism by which cells coordinate their differentiation and migration
is critical to our understanding of many fundamental processes such as wound healing …