Throughout the life sciences, we routinely seek to interpret measurements and observations
using parametrized mechanistic mathematical models. A fundamental and often overlooked …

Sigmoid growth models, such as the logistic, Gompertz and Richards' models, are widely
used to study population dynamics ranging from microscopic populations of cancer cells, to …

Tumour spheroid experiments are routinely used to study cancer progression and treatment.
Various and inconsistent experimental designs are used, leading to challenges in …

Interpreting data using mechanistic mathematical models provides a foundation for
discovery and decision-making in all areas of science and engineering. Developing …

Throughout the life sciences we routinely seek to interpret measurements and observations
using parameterised mechanistic mathematical models. A fundamental and often …

Calibrating mathematical models to describe ecological data provides important insight via
parameter estimation that is not possible from analysing data alone. When we undertake a …

Parameter inference and uncertainty quantification are important steps when relating
mathematical models to real-world observations and when estimating uncertainty in model …

Inert interactions between randomly moving entities and spatial disorder play a crucial role
in quantifying the diffusive properties of a system, with examples ranging from molecules …

Likelihood profiling is an efficient and powerful frequentist approach for parameter
estimation, uncertainty quantification and practical identifiablity analysis. Unfortunately …

Many commonly used mathematical models in the field of mathematical biology involve
challenges of parameter non-identifiability. Practical non-identifiability, where the quality and …