Computational Physiology Course Content


The first 3 days are driven by demonstrating the way computational physiology contributes to the “standard” clinical workflow: data collection, model building, simulation, visualisation, and tying it all together to lead to clinical predictions/outcomes/tools. Each session begins with a lecture from an active computational physiologist presenting current research topics in the exemplar organ system being used in that session.

Day 1

  • Introduction to computational physiology and the standard clinical workflow.
  • Introduction to the two main software tools to be used during the DTP CP module: OpenCOR and MAP Client.
  • Data collection – (heart exemplar)
    • Cardiac lecture
    • Understanding source data formats: DICOM
    • Image processing and manual segmentation, understanding how to interpret clinical data (e.g., MRI images), how generated data will be used.
    • Discussion of automated segmentation methods.

Day 2

  • Model construction – (breast exemplar)
    • Breast lecture
    • Basic introduction to geometric finite element models
    • Creating meshes, fitting meshes
    • Discussion on other aspects of model building (material fields, mathematical models)
  • Simulation – (GI exemplar)
    • GI lecture
    • Introduction to numerical methods (focus on numerical integration)
    • Exploration of different integration methods and their parameterisation
    • Multiscale simulation (electrical propagation in a tissue sheet), and discussion on computational cost/benefit decisions.
    • Creating and simulating ordinary differential equation models
    • Reproducibility and exchange

Day 3

  • Visualisation – (lung exemplar)
    • Lung lecture
    • Viewing and interacting with computational models
    • Types of graphics, appropriateness of graphics for visualising different types and resolutions of data.
    • Time varying visualisations.
    • Exporting visualisations for web distribution/viewing.
    • Incorporating clinical data (images) into visualisations.
  • Complete workflows – (MSK exemplar)
    • MSK lecture
    • Demonstrate workflows which make use of all the above to define workflows going from source data to clinically relevant predictions/outcomes.
    • Discuss best practices for creating complete workflows, especially regarding being able to share and collaborate.

Day 4

  • Best practices in computational physiology
    • Physiome lecture
    • Reproducibility and reusable mathematical models
    • Collaboration, versioning, and model discovery
    • Bond graph-based approach to modularity and thermodynamically balanced model construction.

Day 5

  • Projects (more being added each year)
    • Mechanical parameter estimation
    • Geometric femur fitting
    • Bond graph model of the Hill muscle model

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