| dc.description.abstract | Macromolecules, such as proteins, are the building blocks of the machinery of life, and
therefore are essential to the comprehension of physiological processes. In physiology,
illustrations and animations are often utilized as a mean of communication because they
can easily be understood with little background knowledge. However, their realization
requires numerous months of manual work, which is both expensive and time consuming.
Computational biology experts produce everyday large amount of data that is publicly
available and that contains valuable information about the structure and also the function
of these macromolecules. Instead of relying on manual work to generate illustrative
visualizations of the cell biology, we envision a solution that would utilize all the data
already available in order to streamline the creation process.
In this thesis are presented several contributions that aim at enabling our vision. First,
a novel GPU-based rendering pipeline that allows interactive visualization of realistic
molecular datasets comprising up to hundreds of millions of macromolecules. The
rendering pipeline is embedded into a popular game engine and well known computer
graphics optimizations were adapted to support this type of data, such as level-of-detail,
instancing and occlusion queries. Secondly, a new method for authoring cutaway views
and improving spatial exploration of crowded molecular landscapes. The system relies
on the use of clipping objects that are manually placed in the scene and on visibility
equalizers that allows fine tuning of the visibility of each species present in the scene.
Agent-based modeling produces trajectory data that can also be combined with structural
information in order to animate these landscapes. The snapshots of the trajectories are
often played in fast-forward to shorten the length of the visualized sequences, which
also renders potentially interesting events occurring at a higher temporal resolution
invisible. The third contribution is a solution to visualize time-lapse of agent-based
simulations that also reveals hidden information that is only observable at higher temporal
resolutions. And finally, a new type of particle-system that utilize quantitative models as
input and generate missing spatial information to enable the visualization of molecular
trajectories and interactions. The particle-system produces a similar visual output as
traditional agent-based modeling tools for a much lower computational footprint and
allows interactive changing of the simulation parameters, which was not achievable with
previous methods. | en_US |
