Recent developments in molecular engineering, especially synthetic biology and DNA nanotechnology, are changing the dialog from the design and construction of molecular components to the design and construction of molecular systems. Here, the question of how to manage complexity becomes paramount. Theory attempts to address fundamental questions of complexity, such as: What molecular tasks can be accomplished with a given set of primitives? How efficiently can they be carried out, in terms of time, space, and program size resources? Can reliable behavior be obtained from unreliable molecular components? In this talk, I will give an overview of complexity research on four theoretical models for molecular programming: chemical reaction networks (CRNs), which model randomly-interacting finite state machines; extensions of CRNs to include polymers; self-assembling tile systems, which model generalized crystal growth; molecular robotics, which model interacting molecular motors.

Erik Winfree is an Associate Professor in Computer Science, Computation & Neural Systems and Bioengineering at Caltech. His research concerns the theory and engineering of autonomous biochemical algorithms using in vitro systems of DNA and enzymes, including programmable DNA self-assembly, DNA and RNA conformational switches and devices, and RNA transcriptional circuits. Such systems are envisioned as embedded information-processing and control for bottom-up nanofabrication, nanorobotics, biochemical diagnostics, and other biochemical processes. Winfree is the recipient of numerous awards, including the Feynman Prize for Nanotechnology (2006), the NSF PECASE/CAREER Award (2001), the ONR Young Investigators Award (2001), a MacArthur Fellowship (2000), Tulip prize in DNA Computing, and MIT Technology Review's first TR100 list of "top young innovators" (1999). Prior to joining the faculty at Caltech in 1999, Winfree was a Lewis Thomas Postdoctoral Fellow in Molecular Biology at Princeton, and a Visiting Scientist at the MIT AI Lab. Winfree received a B.S. in Mathematics and Computer Science from the University of Chicago in 1991, and a Ph.D. in Computation & Neural Systems from Caltech in 1998.