Reproducible development of mathematical models of cardiac electrophysiology

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The aim of "systems biology" is to understand how biological systems (e.g. cells, organs, people) work as a collection of parts by using mathematical modelling. We describe the behaviour of the parts in the form of mathematical equations using the laws of physics and chemistry, and then see how the behaviour of larger systems emerges from this. Many systems biology models for specific components have been published, but there remain significant challenges in exploiting them to understand systems as a whole. Which existing models (if any) are most appropriate for a new scientific question? How does each model behave in different situations? How well do they capture what the real systems do? At present it is very difficult to answer these questions without first downloading each model, writing programs to perform different simulated experiments, and then writing more code to compare and visualize the results. There has been nowhere to look up even simple properties for different models. We propose to build upon a pilot implementation of a system that enables such tasks to be done automatically, with results published on a website.Our approach will be demonstrated in perhaps the most mature area of systems biology: the electrical activity of heart cells, for which the first model was published in 1960 and well over 100 models are now available in public databases. The models have been hugely important in giving insights into how the heart works (and what can go wrong due to disease, age or drug side effects) and have helped in developing new treatments.The first step is to compare the different model predictions to measurements from real cells, to tell us whether we really understand how the heart's cells work. We will link real measurements to our recipes for performing equivalent experiments on the computer models, and provide an interface to display the results, indicating how well they agree both qualitatively (general appearance) and quantitatively (how well the numbers match).Cardiac models often have dozens of equations containing hundreds of parameters - key numbers governing how the models behave. How these parameters were worked out from experimental recordings (data) is, more often than not, unclear. Since many models reuse components from previous models, the original methods and data may no longer be available to anyone. This causes big problems for building on these models - if for instance we want to adapt a model to a new cell type, there is no record of which experiments were performed, or how these were analysed to produce the parameters and equations in the final model. We will extend our recipes to capture this information as well, and so be able automatically to re-calibrate a model to a given set of experiments.Crucially, our tools will use the variability in experimental measurements to calculate how models are likely to need to change to capture variations between different cells in a heart, or between different people, and explain how this variation affects predictions.Three case studies will drive development, looking at different kinds of model to give a broad picture of needs. Feedback from the wider community and an external advisory board of experts in cardiac electrophysiology will also be incorporated, building on the success of our first user workshop in September 2015.The final output will be a user-friendly online system - a "Cardiac Electrophysiology Web Lab" - providing an open community resource for researchers to use. We will also write training materials and run further workshops to help these researchers use it. Our resource will make it easier to reuse or extend existing models in appropriate ways, to develop new models, and to understand differences between heart cells. The tools will increase the impact of modelling for replacing animal experimentation and testing, e.g. in drug trials.