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Center for Nonlinear Studies |
At first glance, robots and proteins have little in common. Robots are commonly thought of as tools that perform tasks such as vacuuming the floor, while proteins play essential roles in many biochemical processes. However, the functionality of both robots and proteins is highly dependent on their motions. In the case of robots, complex spaces and many specialized planning methods can make finding feasible motions an expert task. In the case of protein molecules, several diseases such as Alzheimer's, Parkinson's, and Mad Cow Disease are associated with protein misfolding and aggregation. Understanding of molecular motion is still very limited because it is difficult to observe experimentally. Therefore, intelligent computational tools are essential to enable researchers to plan and understand motions. In this talk, we draw from our unique perspective from robotics to present a novel computational approach to approximate complex motions of proteins and robots. Our technique builds a roadmap, or graph, to capture the moveable object's behavior. This roadmap-based approach has also proven successful in domains such as animation and RNA folding. With this roadmap, we can find likely motions (e.g., roadmap paths). For proteins, we demonstrate new learning-based map analysis techniques that allow us to study critical folding events such as the ordered formation of structural features and the time-based population of roadmap conformers. We will show results that capture biological findings for several proteins including Protein G and its structurally similar mutants, NuG1 and NuG2, that demonstrate different folding behaviors. For robots, we demonstrate new learning-based map construction techniques that allow us to intelligently decide where and when to apply specialized planning methods. We will show results that demonstrate automated planning in complex spaces with little to no overhead.
Host: William Hlavacek