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Tag Archives: CellML
ODE solving for cardiac action potential models
Just a quick post to highlight a new paper by Jon Cooper, Ray Spiteri and me, which describes the use of Chaste and CellML to solve cardiac action potential model equations. The paper in Frontiers in Physiology is in a … Continue reading
Posted in Action Potential Models, Numerics, Tissue Simulations
Tagged Action potential, benchmark, CellML, ODE solving
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In-silico Action Potential Prediction Web Portal
This post is to announce that we are making our in-silico action potential prediction web portal (AP-predict) available for public use. The portal is free, you just need to register for an account. We’d be happy to have any feedback on … Continue reading
Initial conditions and steady states
Today I came across a good (or bad, depending on how you look at it) example of initial conditions / steady-states having a massive effect on model behaviour. I think it is generally a good idea to get a model … Continue reading
Introducing the ‘Cardiac Electrophysiology Web Lab’
I’m pleased to announce Jonathan Cooper and I have made our web portal interface to the cardiac electrophysiology virtual experiments publicly available. It’s called ‘Functional Curation for Cardiac Electrophysiology’, which is a bit of a mouthful, so I’m calling it … Continue reading
A vision of the future
Jonathan Cooper and I have been working on a system for describing the simulation you would like to perform with a model*, in a way that is flexible enough to recreate the majority of possible experiments and post-processing, and machine-readable … Continue reading
On the importance of curating models
Most cardiac action potential models are published in a machine-readable markup language called CellML. This is a very good thing, because it unambiguously describes the system of equations that makes up the model, allows people to exchange and use them … Continue reading
Posted in Action Potential Models
Tagged Action potential, Cardiac electrophysiology, CellML, Functional Curation, Models, SED-ML
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