The promise of molecular programming lies in its ability to (i) self-assemble structures with nanometer precision, and (ii) process information autonomously in a biochemical context in order to sense and actuate matter. How do you 'program' an energy landscape so that DNA-based devices operate correctly, yet incur significant kinetic and thermodynamic energy penalties for spurious behavior? I'll focus on two different projects that address this same question in different ways.
(Part I) A very successful example of self-assembly driven by molecular forces is DNA origami. This process can result in the assembly of ~10^10 copies of a designed 2D or 3D shape, with feature resolution of 6 nanometers. By designing the energy landscape of the interaction between a DNA origami shape and a flat surface we demonstrate that single molecules can be placed with orientation that is absolute (all degrees of freedom are specified) and arbitrary (every molecule's orientation is independently specified).
(Part II) Existing molecular computing systems are often slow, error-prone, require bespoke design and weeks of labor to realize experimentally. I will detail our efforts to fix these issues by introducing a molecular breadboard, capable of computing billions of functions. Its purpose is to "scale-up" what is possible with this technology and to "scale-out" its adoption to new contexts. In order to facilitate the rapid design of new circuits from a common molecular broth, we have developed a compiler that takes as input a logic description and provides as output the optimized set of breadboard components necessary to activate the desired logic behavior. By mixing these preexisting components as prescribed, it is possible to achieve fast, autonomous and robust molecular circuits, from conception to implementation, within a single afternoon. Due to the large separation of time scales between designed and spurious computation, we expect the breadboard architecture will open new research directions in molecular sensing, actuation and interfacing with self-assembly systems.
Chris Thachuk is a Banting Fellow awardee and Senior Postdoctoral Researcher at Caltech, with Erik Winfree. Chris works in the areas of DNA computing and molecular programming - how one might compute or build new structures at the nano-scale with bio-molecules such as DNA. He is inspired by the complexity of biology and is interested in applying the principles of computer science and engineering to develop robust programmable matter: by proving fundamental theorems, by designing algorithms, software and new architectures, and by realizing these systems experimentally. Prior to Caltech, Chris was a postdoc at Oxford Computer Science and a James Martin Fellow at the Institute for the Future of Computing, Oxford. He received his PhD in Computer Science from the University of British Columbia, advised by Anne Condon, where he focused on space- & energy-efficient computation and also compressed data structures for computational biology. Much of Chris' computations now happen in a test tube.
Seminar sponsored by the Department of Computer Science and Engineering