English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Throughout the life sciences we routinely seek to interpret measurements andobservations using parameterised mechanistic mathematical models. A fundamentaland often overlooked choice in this approach involves relating the solution ofa mathematical model with noisy and incomplete measurement data. This is oftenachieved by assuming that the data are noisy measurements of the solution of adeterministic mathematical model, and that measurement errors are additive andnormally distributed. While this assumption of additive Gaussian noise isextremely common and simple to implement and interpret, it is often unjustifiedand can lead to poor parameter estimates and non-physical predictions. One wayto overcome this challenge is to implement a different measurement error model.In this review, we demonstrate how to implement a range of measurement errormodels in a likelihood-based framework for estimation, identifiabilityanalysis, and prediction, called Profile-Wise Analysis. This frequentistapproach to uncertainty quantification for mechanistic models leverages theprofile likelihood for targeting parameters and understanding their influenceon predictions. Case studies, motivated by simple caricature models routinelyused in systems biology and mathematical biology literature, illustrate how thesame ideas apply to different types of mathematical models. Open-source Juliacode to reproduce results is available on GitHub.

Implementing measurement error models with mechanistic mathematical  models in a likelihood-based framework for estimation, identifiability  analysis, and prediction in the life sciences