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Image-Based Steering for Integrative Biology

Image-Based Steering for Integrative Biology. Lakshmi Sastry, Richard Wong, Helen Wright with contributions from Ronald Fowler, Sri Nagella and Anjan Pakhira. Acknowledgements. Ken Brodlie and Jason Wood CompuSteer funding Integrative Biology project scientists. Image-based Steering.

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Image-Based Steering for Integrative Biology

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  1. Image-Based Steering for Integrative Biology Lakshmi Sastry, Richard Wong, Helen Wright with contributions from Ronald Fowler, Sri Nagella and Anjan Pakhira

  2. Acknowledgements • Ken Brodlie and Jason Wood • CompuSteer funding • Integrative Biology project scientists

  3. Image-based Steering Integrated display Visualization processing Render Map Filter Simulate X  X

  4. Integrative Biology (IB) • Grid technology to enable in-silico experiments by computational biologists • Combined resources for computation, data management, visualization and data analysis • Focus on fatal diseases – heart and cancer

  5. Example IB Applications Modelling heart electrical activity during arrhythmia: • Tulane whole ventricular model – epicardial potential distribution over heart geometry during shock-induced arrhythmia • Fenton-Karma 4-variable model on 2D slice of tissue

  6. An episode of arrhythmogenesis in ventricular model. The arrhythmia is a figure of eight reentry with one rotor on the anterior (left panel) and another on the posterior (right panel) of the ventricles. The arrows show wave propagation. The scale is saturated, potentials above 20mV are shown in red and below -90mV are shown in blue.

  7. Example IB Applications • In vitro and in-silico models of tumour growth during very early stages • Seamless secure access to very large volumes of image data, processing, simulation and interaction will accelerate understanding of disease process.

  8. Steering for IB Applications • Complex and compute intensive with tens and hundreds of parameters • Verification of models that continue to be refined • Computational exploration of parameter space • Expanding set of simulations and visualization toolkits

  9. Image-based Interaction • Extrinsic parameters (scalars, vectors) mimic widgets but minimise context switching • Parameters intrinsic to the solution graphic, e.g. position specifications • The IB interface provides a layer of abstraction above the clientside libraries for computational steering.

  10. The Case for Server-side Applications • Application may already have steering embedded • Developing a steerable interface and other scalable services for each application does not scale • Difficult to embed steering and other services into certain visualization toolkits • Users want continuity in their visualization toolkits • Minimises changes needed to application software

  11. Client-side Consequences • Keep client generic – configure on set-up to meet application requirements • Needs to handle various geometry and image formats • Application-specific activity e.g. to resolve geometry elements or nodes, takes place server-side

  12. Control panel of widgets Simulation (e.g. CARP) gViz sim. module Client A IB Interface gViz client side Control panel of widgets Visualisation & interactors panel Steer View IB Server Image & image based parameter values from coder/decoder Visualisation toolkit (e.g. Meshalyser) Data Steer View IB Interface gViz client side Visualisation & interactors panel Client B

  13. Collaborative gViz Overview • Parameter changes are passed to all collaborators for visibility (steer/view arrows) • Committed parameters are passed to all collaborators and the simulation, locking interactors • Arrival of data unlocks interactors – implies token-passing • Data streams – not used here – separate results from parameters

  14. Demonstrator Elements • Tumour modelling – growth of ductal carcinoma in breast • Results – time-varying tumour cell counts in axial and radial direction of duct • Steering of nutrient consumption rate and cell-to-duct-wall slip coefficient • Utilises gViz rel.2 (collaborative) for parameter passing, calling Fortran

  15. Visualization Back-end • IRIS Explorer, loosely coupled • Simulation outputs file of results (time step) which triggers visualization • Height-field plot varying in time • height = cell numbers • colour = pressure

  16. Steering nutrient consumption and cell death rate (6MB movie)

  17. OpenGL Interactors

  18. Experiences • Hard to ‘wipe the slate clean’ before starting again • New collaboration helps • Mode is ‘extreme collaboration’ (cf. extreme programming) • Needs dedicated time • Trips - how long is ‘just long enough’?

  19. Remaining Question Marks • Token maintenance over the various architecture pathways • Recombination of 3rd party geometries/images with interactors • Anticipate little problem for extrinsics • Intrinsics more difficult • gViz and multiple simulations?

  20. Remaining Question Marks • How scalable is the architecture really? • Will scientists and steering libraries ever really mix? • What support do scientists need to use steering libraries – documentation, examples, GUI builders?

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