Motivation: This project addressed an emerging challenge in computation: to extend programmatic control over matter and phenomenon at the nanoscale. Nanosystems making use of DNA-based reactions are a promising technique to achieve this: they are feasible to design, simulate and test experimentally. DNA computation systems of increasing complexity have been demonstrated over the past two decades. Most of these systems involve multiple strands of DNA that interact with each other via diffusion based hybridization chemistry. However, there are fundamental limits to diffusion based DNA hybridization computations, particularly due the increased time for larger-scales of complexity. This work studied an alternate paradigm of DNA hybridization-based computations that operate locally on a substrate. Various components of the systems are tethered to an addressable assembly, such as origami or addressable lattices. Locality allows reactions to proceed at higher speed due to increased local concentration of reacting species – this localization can potentially speed up DNA hybridization-based computations by an order of magnitude. Also, since each of the local reaction pathways do not interfere with each other, it is also possible to simultaneously execute multiple pathways in parallel. This also allows one to reuse DNA sequences in spatially separated regions that increase the modularity and scalability of the reactions.

Our Research Work:

Our DNA-based molecular computing devices have the following properties: (a) they execute operations autonomously, without outside-mediated changes, such as thermal cycling, (b) they do not use enzymes, and instead use DNA hybridization reactions, and (c) the locality of our DNA-based molecular computing devices is enforced by the use of tethering to an addressable assembly, such as origami or an addressable DNA lattice.

The research work spanned both theory and experimental techniques, and includes development of (i) biophysical/ mathematical models, (ii) design software, (iii) computational simulations, (iv) small-scale experimental demonstrations. In particular, the work will develop biophysical models of localized hybridization, which will be simulated, and also verified via simple kinetic experiments. The experiments provide crucial data about the rate constants involved in the hybridization chemistry of localized molecules.

Continuing an on-going collaboration with Dr. Andrew Phillips (Microsoft Research Cambridge), this work also creates software systems that simulate localized hybridization networks. The simulation software development was tightly coupled to the experimental progress by constantly refining the simulation models and parameters based on experimental data.

The work experimentally implemented a series of small to moderate scale localized hybridization systems to demonstrate the feasibility and the potential of localized hybridization reactions. Based on the experimental data, the model was further refined.

A major challenge addressed was leaks: the unintended reactions that cause the nanosystem to significantly deviate from its programmed trajectory that might occur in localized hybridization systems. Multiple leak models were proposed and tested in the lab via simple experiments. 

The work also investigated the broader issues of the use of locality to speed-up other related molecular-scale computation processes, including reactions that make use of enzymes, or other protein-based reactions, in addition to DNA hybridization reactions.

Education Impact: The PI placed great emphasis on engaging graduate and undergraduate students in their laboratory research, and are actively involved in training and mentoring students at all educational levels from high school and undergraduate to M.S. and Ph.D. students. In addition, our educational work included summer internships of undergraduates, and also a high school summer internship program. We also provided opportunities for students with diverse backgrounds and interests to gain practical laboratory experience. Students working on this project receive training at Duke University in both computer science and chemistry and DNA-based nanotechnology. Efforts were made to continue to recruit women and minority students into this project at the graduate and post-doctoral level. Opportunities for research training on self-assembling DNA nanostructures provided students with important skills needed to supply the nanoscience savvy workforce needed for future competitive advantage. 

 Interdisciplinary outreach and training: The challenges presented by nanoscience cannot be answered solely by techniques and methods derived from a single science or technology discipline and instead, requires a combination of diverse, but inter-related techniques spanning many disciplines that form the core of an emerging discipline of nanoscience that include synthetic chemistry, physics, material science, biochemistry, computer science, applied mathematics and imaging devices.

This research work involves graduate and undergraduate students mastering a myriad of concepts such as design, modeling and simulations of DNA circuits, software development, various laboratory techniques such as DNA purification and quantification, gel electrophoresis, fluorescence spectrometry, atomic force microscopy, enzymatic protocols and other general lab skills. Beyond these multidisciplinary skills, the students have unique opportunities to engage in interdisciplinary research - since the ongoing research requires a creative combination of these disparate disciplines.

Also, PI Prof. John Reif, taught a course on DNA self-assembly and computation that included the design and simulations of DNA hybridization systems and include error-correction as a fundamental principle in designing nanosystems. 

Last Modified: 08/25/2016
Modified by: John H Reif