Mathematical models are routinely calibrated to experimental data, with goals ranging from
building predictive models to quantifying parameters that cannot be measured. Whether or …

Major computational challenges exist in relation to the collection, curation, processing and
analysis of large genomic and imaging datasets, as well as the simulation of larger and …

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 …

Many models of gene expression do not explicitly incorporate a cell cycle description. Here,
we derive a theory describing how messenger RNA (mRNA) fluctuations for constitutive and …

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

Agent-based models (ABMs) are readily used to capture the stochasticity in tumour
evolution; however, these models are often challenging to validate with experimental …

Abstract Models of stochastic processes are widely used in almost all fields of science.
Theory validation, parameter estimation, and prediction all require model calibration and …

Background The global impact of COVID-19 and the country-specific responses to the
pandemic provide an unparalleled opportunity to learn about different patterns of the …

The problem of flickering trajectories in standard kinetic Monte Carlo (kMC) simulations
prohibits sampling of the transition path ensembles (TPEs) on Markovian networks …

Stochastic differential equations (SDE) are a powerful tool to model biological regulatory
processes with intrinsic and extrinsic noise. However, numerical simulations of SDE models …