PAVA: physiological and anatomical visual analytics for mapping of tissue-specific concentration and time-course data
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We describe the development and implementation of a Physiological and Anatomical Visual Analytics tool (PAVA), a web browser-based application, used to visualize experimental/simulated chemical time-course data (dosimetry), epidemiological data and Physiologically-Annotated Data (PAD). Using continuous color mapping scheme both spatial (organ shape and location) and temporal (time-course/kinetics) data was cast onto an abstract, layered, 2D visual representation of the human anatomy and physiology. This approach is aligned with the compartment-level of detail afforded by Physiologically-Based Pharmacokinetic (PBPK) modeling of chemical disposition. In this tutorial we provide several illustrative examples of how PAVA may be applied: (1) visualization of multiple organ/tissue simulated dosimetry of a previously published oral exposure route ethanol PBPK model, (2) visualization of PAD such as organ-specific disease time-lines or (3) tissue-specific mRNA expression-level profiles (e.g. phase I/II metabolic enzymes and nuclear receptors) to draw much needed molecular biological conclusions at organ-level resolution conducive to model development. Furthermore, discussion is raised on how graphical representations of PBPK models, and the use of PAVA more generally to visualize PAD, can be of benefit. We believe this novel platform-independent tool for visualizing PAD on physiologically-relevant representations of human anatomy will become a valuable visual analytic addition to the tool-kits of modern exposure scientists, computational biologists, toxicologists, biochemists, molecular biologists, epidemiologists and pathologists alike in visually translating, representing and mining complex PAD relationships required to understand systems biology or manage chemical risk.
KeywordsPhysiologically-annotated data Dosimetry Visualization Visual analytics Anatomical Physiological Server-side application Model animation Concentration time-course Disease progression timelines Model rendering PBPK PBTK
The authors gratefully acknowledge Rachael Brady (Duke University Visual Technology Group) for providing the opportunity to present to the academic visualization community and receive insightful feedback (see http://vis.duke.edu/FridayForum/09Fall.html and http://lectopia.oit.duke.edu/ilectures/ilectures.lasso?ut=193&id=21022) and Dr. Shane Peterson and Dr. Elin Ulrich (US-EPA) for thorough criticism and suggestions towards the manuscript.
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