Programmable Stochastic Self-Assembly

Professor Eric Klavins

University of Washington

Department of Electrical Engineering

We consider the control of programmable self-assembling systems whose dynamics are governed by stochastic reaction-diffusion dynamics. In our system, particles may decide the outcomes of local reactions initiated by the environment, thereby steering the global system to produce a desired assembly type. I will describe the construction of local rule sets using graph grammars and methods by which the global properties of the resulting systems can be guaranteed.  Then, based on measured natural reaction rates, we describe a method that automatically generates the best rule set for the particles so as to maximize the yield in the system – essentially an example of metabolic pathway engineering in a unique setting. We demonstrate the design method using a variety of examples, including with our self-assembling robot test-bed.

Friday, November 10, 2006

3:30 – 4:30 p.m.

1500 EECS