Evidence map›Paper›PMID 40811762›Full record

ArticlePLoS computational biology2025

IonBench: A benchmark of optimisation strategies for mathematical models of ion channel currents.

Matt J Owen, Gary R Mirams

Abstract read
In one paragraph

Article in PLoS computational biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Matt J OwenCentre for Mathematical Medicine and Biology, School of Mathematical Sciences, University of Nottingham, Nottingham, United Kingdom.ORCID 0000-0003-1430-5473
Gary R MiramsCentre for Mathematical Medicine and Biology, School of Mathematical Sciences, University of Nottingham, Nottingham, United Kingdom.ORCID 0000-0002-4569-4312

Funding

Wellcome Trust
6 · The paper itself

Abstract

Ion channel models present many challenging optimisation problems. These include unidentifiable parameters, noisy data, unobserved states, and a combination of both fast and slow timescales. This can make it difficult to choose a suitable optimisation routine a priori. Nevertheless, many attempts have been made to design optimisation routines specifically for ion channel models, however, little work has been done to compare these optimisation approaches. We have developed ionBench, an open-source optimisation benchmarking framework, to evaluate and compare these approaches against a standard set of ion channel optimisation problems. We included implementations of thirty-four unique optimisation approaches that have been previously applied to ion channel models and evaluated them against the ionBench test suite, consisting of five parameter optimisation problems derived from the cardiac ion channel literature. Each optimisation approach was initiated from multiple starting parameters and tasked with reproducing a problem-specific simulated dataset. Through ionBench, we tracked and evaluated the performance of these optimisations, identifying the expected run time until a successful optimisation for each approach, which was used for comparisons. Finally, we used these results, in addition to other literature results, to identify a new efficient approach. Its use could reduce computation time by multiple orders of magnitude, while also improving the reliability of ion channel parameter optimisation.

Indexed as

Ion ChannelsModels, BiologicalAlgorithmsAnimalsBenchmarkingComputational BiologyComputer SimulationHumansIon Channel GatingSoftwareIon Channels

Identifiers

PMID40811762
PMCPMC12373289

What Socratic holds

Textmetadata
LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.