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 Plus annual

Presents the access of premium content as premium feature

Premium Content

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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Computer simulations have become an important tool across the biomedicalsciences and beyond. For many important problems several different models orhypotheses exist and choosing which one best describes reality or observed datais not straightforward. We therefore require suitable statistical tools thatallow us to choose rationally between different mechanistic models of e.g.signal transduction or gene regulation networks. This is particularlychallenging in systems biology where only a small number of molecular speciescan be assayed at any given time and all measurements are subject tomeasurement uncertainty. Here we develop such a model selection framework basedon approximate Bayesian computation and employing sequential Monte Carlosampling. We show that our approach can be applied across a wide range ofbiological scenarios, and we illustrate its use on real data describinginfluenza dynamics and the JAK-STAT signalling pathway. Bayesian modelselection strikes a balance between the complexity of the simulation models andtheir ability to describe observed data. The present approach enables us toemploy the whole formal apparatus to any system that can be (efficiently)simulated, even when exact likelihoods are computationally intractable.

Simulation-based model selection for dynamical systems in systems and population biology