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Chaste: using agile programming techniques to develop computational biology software.

Joe Pitt-Francis1, Miguel O Bernabeu, Jonathan Cooper

  • 1Oxford University Computing Laboratory, Wolfson Building, University of Oxford, Parks Road, Oxford OX1 3QD, UK. joe.pitt-francis@comlab.ox.ac.uk

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|June 21, 2008
PubMed
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Test-driven agile methods offer a more suitable approach for developing scientific software, particularly in cardiac modeling. This approach enhances code malleability for future extensions and improved numerical techniques.

Area of Science:

  • Physiome modelling
  • Computational biology
  • Scientific software development

Background:

  • Cardiac modelling software is mature, serving as a benchmark for wider physiome community needs.
  • Existing software for simulating cardiac electrical activity is evaluated for numerical algorithms, efficiency, usability, robustness, and extensibility.

Purpose of the Study:

  • To introduce advanced cardiac electrical activity simulation software.
  • To compare traditional academic software development with test-driven agile methods.
  • To present a case study of agile methods in computational biology software development.

Main Methods:

  • Review of advanced cardiac simulation software.
  • Description and argumentation for test-driven agile methodologies.

Related Experiment Videos

  • Case study analysis of the Cancer, Heart and Soft Tissue Environment (CHaEST) project.

    • Main Results:

    • Agile methods show potential advantages for scientific software development compared to traditional approaches.
    • The CHaEST project demonstrates the practical application and experiences with agile programming.
    • Identified areas for improvement in current cardiac modeling software, particularly regarding malleability.

    Conclusions:

    • Test-driven agile methods are recommended for scientific software development.
    • Current cardiac modeling software may not fully meet evolving community needs.
    • Increased code malleability is crucial for incorporating new mathematical models and numerical techniques.