Presentation on theme: "Image-Based Steering for Integrative Biology Lakshmi Sastry, Richard Wong, Helen Wright with contributions from Ronald Fowler, Sri Nagella and Anjan Pakhira."— Presentation transcript:
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 SimulateFilterMapRender Visualization processing Integrated display X X
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
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
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.
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.
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
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.
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
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
IB Interface Visualisation & interactors panel Control panel of widgets gViz client side IB Interface Visualisation & interactors panel Control panel of widgets gViz client side Client A Simulation (e.g. CARP) gViz sim. module SteerSteer ViewView Visualisation toolkit (e.g. Meshalyser) IB Server Data Image & image based parameter values from coder/decoder SteerSteer ViewView Client B
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
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
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
Steering nutrient consumption and cell death rate (6MB movie)
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?
Remaining Question Marks Token maintenance over the various architecture pathways Recombination of 3 rd party geometries/images with interactors Anticipate little problem for extrinsics Intrinsics more difficult gViz and multiple simulations?
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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