World in a Cell – Protein Visualization and Computational Biology
A New Visual Design Language for Biological Structures in a Cell
Summary
As a component of our initiative to construct a spatiotemporal model for the pancreatic β-cell, we are crafting an immersive encounter known as “World in a Cell.” This experience is designed to facilitate the integration and development of innovative educational tools. To achieve this goal, we have introduced a novel visual design concept employing tetrahedral building blocks to portray the structural characteristics of biological molecules and organelles within densely populated cellular surroundings. The utilization of this tetrahedral language enhances the efficiency of animation and user interaction within the immersive environment.
This work resulted in a published paper in the leading journal for computation biology Structure. Both Shaoyu Su and I and the work that we did are cited, and we are a portion of the patent holders of this new scientific visualization methodology.
World in a Cell is a project from the World Building Lab in the School of Cinematic Arts at the University of Southern California. The project is headed by renowned Production Designer Alex McDowell (Minority Report) and leading Computational Biologist Helen Berman. The project re-examines how we scientifically depict the structure of proteins. Through a virtual reality app, it seeks to educate users about the world of proteins.
Using Houdini for Protein Visualization and Computational Biology.
Shaoyu Su and I were responsible for creating a working procedural prototype that would interpret the data provided by the Protein Database. Using Python and Houdini we built a system that would parse the file provided by the .cif file created by the protein database. Once the cartesian data was plotted we then ran the data through a genetic algorithm that would equalize that coordinate data to create a visualization of perfect tetrahedral geometry. Our code would measure the deviation and fit within an acceptable RMS data variance.