Browsing by Subject "DNA nanotechnology"

DNA origami has been a paradigm-shifting technique for synthesizing and manipulating matter with nanoscale precision. The simple design principle of using numerous short (<100 nts) oligonucleotides to "fold" a long (>1000 nts) DNA strand achieved both simplicity in design and greatly increased yields in comparison to previous motifs for DNA nanostructure design. Various approaches have been explored that have resulted in DNA nanostructures rapidly growing in mass and complexity while also becoming more accessible for a wide scientific community, such as developing computer-aided design graphical user interfaces, establishing design principles for classes of structures with algorithmic regularity, and refining synthesis strategies and the respective design criteria to exploit them.

These directions are all fundamentally a straight extension of the DNA origami technique and pursuits towards large, functional DNA origami have been amply rewarded. Yet due to the nature of how a primary driving factor of scaling designs upwards has been the exploitation of repeatable motifs, several assumptions underlie conventional strategies for the DNA origami design of complex shapes. This thesis formally classifies a geometry of curved DNA origami nanostructures and discusses how such structures do not align with existing assumptions for DNA nanostructure design. While it is class of structures that has high biotechnological relevance, the tedium of design challenges arising from this departure have limited accessibility and enthusiasm for utilizing them. To achieve greater functional relevance, DNA origami must undoubtedly retread on the establishment of strategies for scaling up mass and shape complexity in DNA nanostructures; this time beyond regular, repeating subunits, and towards supramolecular assemblies with distinct, bespoke geometric features. As such, this thesis entreats an approach towards formalizing local and global properties in DNA origami design that can be quantified and characterized for their effects on DNA nanostructure yield and stability. Thus, a generalized strategy for DNA origami design can be born.

This thesis first consolidates and proposes a hierarchy of properties active in DNA origami design. It then suggests and evaluates two heuristic optimization algorithms to attempt a multi-variable optimization of those properties to achieve rapid generation of oligonucleotide sequences to generate desired DNA origami shapes. This thesis then discusses the existing challenges and potential applications of curved DNA origami nanostructures. Lastly, the application of the aforementioned optimization algorithms are applied to generate examples in this class of nanostructures, and the results are hither reported and discussed.

DNA nanotechnology is a fascinating field that eschews using DNA as an information storage medium and instead uses it as a nanoscale structural material, taking advantage of the canonical base pairing rules to fold DNA into shapes, patterns, and mechanical devices 10,000 times smaller than a human hair. Over the 40 years of the field's existence, DNA nanotechnology has progressed from building simple wireframe structures to a full-blown nanoengineering ecosystem with the ability to construct logic-gated nanoscale drug delivery vehicles, computing devices, robots, and more. Key to the development of the field has been the growing ability to predict the behavior of DNA nanostructures. However, much is still not understood these devices' self-assembly and dynamic behaviors. The reason for this is that DNA interacts on a short length scale and a long timescale, and many processes occur far from equilibrium, both of which make modeling their behavior challenging. This dissertation presents three projects employing mesoscopic simulations, statistical mechanics, and numerical free energy landscape calculations to provide access to these length and time scales in order to better understand the self-assembly and organization of DNA nanostructures. Specifically, mesoscopic simulations are used to directly simulate the self-assembly of DNA nanostructures and understand the mechanism of their folding; lattice simulations are used to understand the phase behavior of arrays of molecular rotors made from DNA; and geometric calculations and Brownian dynamics simulations are used to computationally derive a bottom-up technique for templating heterogeneous DNA origami species on a single lithographically-defined template.

This dissertation presents several novel architectures for DNA computing from different perspectives including analog DNA circuits, polymerase-based DNA logic circuits, and localized DNA-based biomolecular reaction networks on cancer cell membranes.

Chapter 2 presents an architecture for the systematic construction of DNA circuits for analog arithmetic computation based on DNA strand displacement. The elementary gates in our architecture include addition, subtraction, and multiplication gates. The input and output of these gates are analog, which means that they are directly represented by the concentrations of the input and output DNA strands respectively, without requiring a threshold for converting to Boolean signals. We provide detailed domain designs and kinetic simulations of the gates to demonstrate their expected performance. Based on these gates, we describe how DNA circuits to compute polynomial functions of inputs can be built. Using Taylor Series and Newton Iteration methods, functions beyond the scope of polynomials can also be computed by DNA circuits built upon our architecture.

Chapter 3 focuses on an architecture to build compact DNA strand displacement circuits to compute a broad scope of functions in an analog fashion. A circuit by this architecture is composed of three autocatalytic amplifiers, and the amplifiers interact to perform computation. We show DNA circuits to compute functions sqrt(x), ln(x) and exp(x) for x in tunable ranges with simulation results. A key innovation in our architecture, inspired by Napier’s use of logarithm transforms to compute square roots on a slide rule, is to make use of autocatalytic amplifiers to do logarithmic and exponential transforms in concentration and time. In particular, we convert from the input that is encoded by the initial concentration of the input DNA strand, to time, and then back again to the output encoded by the concentration of the output DNA strand at equilibrium. This combined use of strand-concentration and time encoding of computational values may have impact on other forms of molecular computation.

Chapter 4 introduces an architecture for fast diffusion-based DNA logic circuits based on Bst 2.0 DNA polymerase and single-stranded logic gates. Each gate consists of only single-stranded DNAs that are easy to design and robust to environmental changes. Large-scale logic circuits can be constructed from the gates by simple cascading strategies. The logic gates and circuits respond in minutes (in terms of half-completion time) compared to hours in prior architectures, providing a very substantial speed-up over prior DNA computing architectures. In particular, we have demonstrated a large-scale DNA logic circuit that computes (the floor of) the square root of 4-bit input numbers. The scale of this circuit is comparable to the largest DNA logic circuit to date (that circuit computes the same function as ours) in terms of the number of gates, and the half-completion time of computing by our circuit is only several minutes, compared to hours by the prior circuit.

Chapter 5 proposes an architecture to program localized DNA-based biomolecular reaction networks on cancer cell membranes. Each node in a network targets a designated cancer cell membrane receptor via aptamer-receptor binding. If all nodes find their corresponding receptors on a cancer cell, the network can start to function by adding initiator DNA strands. Various types of circuits have been experimentally demonstrated from simple linear cascades to reaction networks of more complex structures. These localized reaction networks can be used for medical applications such as cancer detection and therapies.

DNA nanorobots are molecular-scale synthetic devices composed primarily of DNA, that can execute a variety of operations. In the last decades, there have been considerable advances in DNA nanorobots, which have been demonstrated to perform autonomous walking, maze traversal, and cargo delivery activities. A major challenge in the design of these DNA nanorobots is to increase the diversity of the types of activities they can perform, in spite of practical limitations on the complexity of each individual DNA-nanobot. This project takes inspiration from insects such as ants and honeybees, which perform a wide variety of relatively complex organized behaviors with very limited individual brains. Mobile DNA nanorobots (which we also term DNA walkers) are a class of DNA nanorobots which can move over a nanotrack composed of DNA stepping stones. The nanotrack may be 1D or 2D and may be either self-assembled DNA nanostructure or a set of DNA strands affixed to a surface. Autonomous mobile DNA nanorobots (also termed autonomous DNA walkers) are mobile DNA nanorobots that locomote autonomously. Here we propose social DNA nanorobots, which are autonomous mobile DNA nanorobots that execute a series of pair-wise interactions between pairs of DNA nanorobots that determine an over-all desired outcome behavior for the group of nanorobots. We present various designs for social DNA nanorobots that provide diverse behaviors including, Walking, Self-avoiding Walking, Flocking, Guarding, Attacking, Voting by Assassination, and Foraging.