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A key feature of biological systems is the ability to evolve in a changing environment. While man-made systems can be engineered to be responsive and to some degree autoregulatory, their behavior does not match the level of complexity and robustness found in living systems. For example, so-called smart materials--such as piezoelectric materials or shape memory alloys--can reversibly switch between different states as external conditions change but cannot dynamically adapt to their environment.
The aim of my research is to design intelligent materials: self-optimizing systems that can evolve in much the same way as a biological system, adapting to new environmental conditions and learning to better perform a function over time. The primary motivation for this research is not necessarily a desire to construct a chemically novel material geared towards a specific engineering application. Rather, the end goal is the development of a general framework for evaluating and designing intelligent behavior in the context of a population of molecules.
I use a combined modeling and experimental approach to investigate intelligent behavior in chemical systems, guided by several overarching questions:
In particular, my research focuses on DNA-based systems. For the purpose of rationally designing a system from the ground up, DNA is a natural choice of building block, largely because Watson Crick base pairing is one of the most robust and well characterized recognition motifs found in nature. From a design perspective, DNA's four letter alphabet provides a combinatorial sequence design space while the highly sequence dependent nature of DNA thermodynamics/kinetics offers a great deal of fine control.
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