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

When mathematical biology models are used to make quantitative predictionsfor clinical or industrial use, it is important that these predictions comewith a reliable estimate of their accuracy (uncertainty quantification).Because models of complex biological systems are always large simplifications,model discrepancy arises - where a mathematical model fails to recapitulate thetrue data generating process. This presents a particular challenge for makingaccurate predictions, and especially for making accurate estimates ofuncertainty in these predictions. Experimentalists and modellers must choosewhich experimental procedures (protocols) are used to produce data to traintheir models. We propose to characterise uncertainty owing to model discrepancywith an ensemble of parameter sets, each of which results from training to datafrom a different protocol. The variability in predictions from this ensembleprovides an empirical estimate of predictive uncertainty owing to modeldiscrepancy, even for unseen protocols. We use the example of electrophysiologyexperiments, which are used to investigate the kinetics of the hERG potassiumion channel. Here, `information-rich' protocols allow mathematical models to betrained using numerous short experiments performed on the same cell. Typically,assuming independent observational errors and training a model to an individualexperiment results in parameter estimates with very little dependence onobservational noise. Moreover, parameter sets arising from the same modelapplied to different experiments often conflict - indicative of modeldiscrepancy. Our methods will help select more suitable mathematical models ofhERG for future studies, and will be widely applicable to a range of biologicalmodelling problems.

Empirical quantification of predictive uncertainty due to model  discrepancy by training with an ensemble of experimental designs: an  application to ion channel kinetics