Dr. Lydia Tapia, University of New Mexico
Robotics Inspired Methods for Modeling Molecular Motion: From Molecular Docking to Antibody Assembly
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 molecules play essential roles in biochemical processes. However, the functionality of both robots and molecules is highly dependent on their motions. Despite the dramatically different structures, complexity, and scales of robots and molecules, structural representations can be used that cross both domains, thus enabling computationally efficient motion simulation. In this talk, we explore motions of molecules and demonstrate how algorithms, tools, and ideas from robotics can be applied to molecular motion. To begin we study the problem of antibody-allergen assembly, a signaling precursor of an allergic response where antibodies bind to allergens to form multi-molecular structures. For this problem, we explore the use of reduced resolution molecular models in order to capture experimental results including aggregation patterns and cell signaling responses. Our computationally efficient solutions to this problem are inspired by multi-agent cooperative robotic motion. Our work in reduced resolution models has also been applied to the analysis of flexible structures imaged by Cryo EM. In this application we will discuss how methods to fit semi-flexible molecular models to Cryo EM tomograms are inspired by robotic conformational search and graphics. Finally, we have incorporated our work in molecular interaction modeling into a molecular docking game where users can feel atomic forces while they dock molecules. Robotics-based algorithmic solutions provide ways of aggregating player data to be able to identify low energy trajectories. This enables crowd-sourced solutions to high-dimensional molecular motion problems.
Lydia Tapia is an Associate Professor in the Department of Computer Science at the University of New Mexico. She received her Ph.D. in Computer Science from Texas A&M University and her B.S. in Computer Science from Tulane University. Her research contributions are focused on the development of computationally efficient algorithms for the simulation and analysis of high-dimensional motions for robots and molecules. Specifically, she explores problems in computational structural biology, motion under stochastic uncertainty, and reinforcement learning. Based on this work, she has been awarded two patents, one on a novel unmanned aerial vehicle design and another on a method to design allergen treatments. Lydia is the recipient of the 2016 Denice Denton Emerging Leader ABIE Award from the Anita Borg Institute, a 2016 NSF CAREER Award for her work on simulating molecular assembly, and the 2017 Computing Research Association Committee on the Status of Women in Computing Research (CRA-W) Borg Early Career Award.