Web Lab: Advancing Quantitative Model Development and Reproducible Experiments

1. The Web Lab: current status & future plansGary Mirams CellML Workshop 2018

2. An imagined conversation in 2010 Experimentalist: I’ve recorded some action potentials from a dog myocyte at 2Hz. Are your models any good at predicting them? Modeller: Er, let’s have a look at these papers on dog models… Oh. None of them have a 2Hz action potential in them. Experimentalist: Well, where can we look them up? Modeller: Er, you can’t. I’ll have to download them all, and write some code / or use a GUI to get you the 2Hz traces. Give me a day or two. Experimentalist: Are you telling me your so-called “most mature field in systems biology” has no way to show me what a 2Hz action potential looks like for your dog models? Modeller: Er… afraid so.

3. Web Lab 1

4. Motivation C = closed O = open I = inactivated Different published structures for Ikr models (cardiac potassium ion current)

5. Different models, different predictions Some variation expected, but which model should e.g. the FDA use?

6. Motivation for Web Lab 1 • Comparing model behaviours • Different models in the same situation • Or one model in different situations • Assess suitability for a new study • Record process of ‘model->figure’ for reproducibility

7. Motivation for Web Lab 1

8. What does Web Lab 1 enable?

9. Here are the dog 2Hz action potentials https://goo.gl/NfUVxw

10. Key features summary • Consistent application of a protocol to any model • Interface described at the level of biophysical concepts • Units conversions are all handled automatically • Specify model inputs and outputs • Simulator works out which equations it needs for that simulation • Replace components • For example encode your own stimulus protocol, or apply voltage clamps, even to alter or add new equations (e.g. change/add ionic buffering to match an experiment) • Includes all the post-processing and plotting instructions • Ability to do complex parameter sweeps, analysis, etc.

11. Things left to perfect • Annotations – at present our own ‘oxmeta’ simple ontology stored in the CellML files as RDF tags. But we’ve had to copy models from the CellML repo: https://github.com/Chaste/cellmlCan we work out how to annotate the official CellML repo files instead and get started on that? • Protocol language – quite tricky to learn/write, and very tricky to debug.Is it desirable to have such a markup language for protocol definition that includes postprocessing?

12. Web Lab 2

13. What next? A vision of the future Knowledge about mechanisms is captured in quantitative models Best experiments to do are therefore the ones that best [select and] parameterise the model Provide these to experimentalists Automate model development and evaluation of predictive power Deploy in the Virtual Physiological Human!

14. Motivation for Web Lab 2 Reproducible Model Development: https://doi.org/10.1016/j.pbiomolbio.2018.05.011 Experimental data Parameter fitting Technology stack revamp

15. Web Lab 2 – adding data! • Will also be able to compare between experimental datasets • Ontology-based search and selection

16. Web Lab 2 – reproducible model building

17. Pints PINTs – Probabilisitic Inference for Noisy Timeseries https://github.com/pints-team/pints A back end to do the fitting aspects, and log what has been done into a Web Lab fitting spec.

18. Acknowledgments • Project team • David Gavaghan • Jonathan Cooper • Gary Mirams • Michael Clerx • [Aidan Daly] • Asif Tamuri • Helen Sherwood-Taylor • Collaborators • Steve Niederer (KCL) • Rick Gray (FDA) • Kylie Beattie (GSK)