Identifiability analysis for stochastic differential equation models in systems biology
Mathematical models are routinely calibrated to experimental data, with goals ranging from
building predictive models to quantifying parameters that cannot be measured. Whether or …
building predictive models to quantifying parameters that cannot be measured. Whether or …
Julia for biologists
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 …
analysis of large genomic and imaging datasets, as well as the simulation of larger and …
Practical parameter identifiability for spatio-temporal models of cell invasion
MJ Simpson, RE Baker… - Journal of the …, 2020 - royalsocietypublishing.org
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 …
model of a scratch assay. Experimental data involve fluorescent cell cycle labels, providing …
Effects of cell cycle variability on lineage and population measurements of messenger RNA abundance
R Perez-Carrasco, C Beentjes… - Journal of the Royal …, 2020 - royalsocietypublishing.org
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 …
we derive a theory describing how messenger RNA (mRNA) fluctuations for constitutive and …
Using experimental data and information criteria to guide model selection for reaction–diffusion problems in mathematical biology
Reaction–diffusion models describing the movement, reproduction and death of individuals
within a population are key mathematical modelling tools with widespread applications in …
within a population are key mathematical modelling tools with widespread applications in …
Calibration of agent based models for monophasic and biphasic tumour growth using approximate Bayesian computation
Agent-based models (ABMs) are readily used to capture the stochasticity in tumour
evolution; however, these models are often challenging to validate with experimental …
evolution; however, these models are often challenging to validate with experimental …
Multifidelity multilevel Monte Carlo to accelerate approximate Bayesian parameter inference for partially observed stochastic processes
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 …
Theory validation, parameter estimation, and prediction all require model calibration and …
Hindsight is 2020 vision: a characterisation of the global response to the COVID-19 pandemic
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 …
pandemic provide an unparalleled opportunity to learn about different patterns of the …
Efficient and exact sampling of transition path ensembles on Markovian networks
DJ Sharpe, DJ Wales - The Journal of Chemical Physics, 2020 - pubs.aip.org
The problem of flickering trajectories in standard kinetic Monte Carlo (kMC) simulations
prohibits sampling of the transition path ensembles (TPEs) on Markovian networks …
prohibits sampling of the transition path ensembles (TPEs) on Markovian networks …
Parameter estimation and uncertainty quantification using information geometry
In this work, we:(i) review likelihood-based inference for parameter estimation and the
construction of confidence regions; and (ii) explore the use of techniques from information …
construction of confidence regions; and (ii) explore the use of techniques from information …