Optimal Rules for Programmed Stochastic Self-Assembly

We consider the control of programmable selfassembling systems whose dynamics are governed by stochastic reaction-diffusion dynamics. In our system, particles may decide the outcomes of reactions initiated by the environment, thereby steering the global system to produce a desired assembly type. We describe a method that automatically generates a program maximizing yield based on tuning the rates of experimentally determined reaction pathways. We demonstrate the method using theoretical examples and with a robotic testbed. Finally, we present, in the form of a graph grammar, a communication protocol that implements the generated programs in a distributed manner.