Stochasticity is a key characteristic of intracellular processes such as gene regulation and
chemical signalling. Therefore, characterizing stochastic effects in biochemical systems is …

Abstract Sparse Identification of Nonlinear Dynamics (SINDy) is a method of system
discovery that has been shown to successfully recover governing dynamical systems from …

A huge variety of mathematical models have been used to investigate collective cell
migration. The aim of this brief review is twofold: to present a number of modelling …

We present a novel weak formulation and discretization for discovering governing equations
from noisy measurement data. This method of learning differential equations from data fits …

Biologically-informed neural networks (BINNs), an extension of physics-informed neural
networks, are introduced and used to discover the underlying dynamics of biological …

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 …

Tissue growth in bioscaffolds is influenced significantly by pore geometry, but how this
geometric dependence emerges from dynamic cellular processes such as cell proliferation …

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 …

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

The Fisher–Kolmogorov–Petrovsky–Piskunov model, also known as the Fisher–KPP model,
supports travelling wave solutions that are successfully used to model numerous invasive …