Single living cells can chase targets, change shape in response to functional cues, and self-replicate. How can a simple group of molecules inside a cell orchestrate such complex behaviors? While individual molecules may have limited function, a network of chemical reactions can simulate a Turing machine, and it is widely believed that the complex control algorithms in cells are the result of networks of interactions involving many molecules. I’ll describe how we can investigate the power of such networks of molecular interactions by trying to reimplement computations, construction processes and control algorithms as molecular interactions involving synthetic DNA molecules. The chemistry and structure of DNA is well-understood, and we can engineer specific interactions between DNA molecules by designing their sequences. We can therefore focus on the dynamics of systems of interactions rather than the chemistry of individual interactions. I’ll show how we can use DNA to build a self-replicator whose alphabet is a series of DNA blocks and program a set of molecules to execute a “search and capture” process that can form tether between two points of unknown location. From these examples we learn that molecular reaction networks are surprisingly powerful: a relatively small set of molecules can both compute and learn arbitrarily complex patterns, and even though molecular interactions are stochastic and unreliable, we can design systems of molecules whose behavior is robust. I’ll close by describing some new work designing computational “morphogenesis” processes that could allow us to produce complex standing chemical patterns in 3 dimensions.
Speaker Biography
Rebecca Schulman studied computer science and mathematics at MIT, where she worked with Gerald Sussman’s group as part of the amorphous computing project. After a several year hiatus in Silicon Valley in which she helped start a company focused on natural language access to databases and where she wrote Linux software at Eazel, Dr. Schulman returned to graduate school to study molecular computation. She received her PhD in computation and neural systems from Caltech in 2007, where she studied under Erik Winfree. From 2008 to 2011 she was a Miller research fellow in the physics department at the University of California Berkeley. Dr. Schulman is currently an assistant professor in chemical and biomolecular engineering at Johns Hopkins; her group focuses on the design and characterization of complex self-assembly processes and the construction computational materials and structures.