View source for From Robots to Molecules: Intelligent Motion Planning and Analysis with Probabilistic Roadmaps

From Robots to Molecules: Intelligent Motion Planning and Analysis
with Probabilistic Roadmaps

Abstract:

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.

Bio:

Lydia Tapia is a Computing Innovation Post Doctoral Fellow in the
Institute for Computational Engineering and Sciences at the University
of Texas at Austin working with Prof. Ron Elber.  She received a
Ph.D. in 2009 from Texas A&M University after working with Prof. Nancy
Amato.  At A&M she participated as a fellow in the Molecular
Biophysics Training and GAANN programs and was awarded a Sloan
Scholarship and a P.E.O. Scholars Award. Lydia also attended Tulane
University where she received a BS in Computer Science with academic
and research honors.  Prior to graduate school, she worked as a member
of technical research staff as part of the Virtual Reality Laboratory
at Sandia National Laboratories.  More information about Lydia Tapia's
research and publications can be found at http://parasol.tamu.edu/~ltapia