Browsing by Author "Reif, John H"

The control of matter and phenomena at the nanoscale is fast becoming one of the most important challenges of the 21st century with wide-ranging applications from energy and health care to computing and material science. Conventional top-down approaches to nanotechnology, having served us well for long, are reaching their inherent limitations. Meanwhile, bottom-up methods such as self-assembly are emerging as viable alternatives for nanoscale fabrication and manipulation.

A particularly successful bottom up technique is DNA self-assembly where a set of carefully designed DNA strands form a nanoscale object as a consequence of specific, local interactions among the different components, without external direction. The final product of the self-assembly process might be a static nanostructure or a dynamic nanodevice that performs a specific function. Over the past two decades, DNA self-assembly has produced stunning nanoscale objects such as 2D and 3D lattices, polyhedra and addressable arbitrary shaped substrates, and a myriad of nanoscale devices such as molecular tweezers, computational circuits, biosensors and molecular assembly lines. In this dissertation we study multiple problems in the theory, simulations and experiments of DNA self-assembly.

We extend the Turing-universal mathematical framework of self-assembly known as the Tile Assembly Model by incorporating randomization during the assembly process. This allows us to reduce the tile complexity of linear assemblies. We develop multiple techniques to build linear assemblies of expected length N using far fewer tile types than previously possible.

We abstract the fundamental properties of DNA and develop a biochemical system, which we call meta-DNA, based entirely on strands of DNA as the only component molecule. We further develop various enzyme-free protocols to manipulate meta-DNA systems and provide strand level details along with abstract notations for these mechanisms.

We simulate DNA circuits by providing detailed designs for local molecular computations that involve spatially contiguous molecules arranged on addressable substrates via enzyme-free DNA hybridization reaction cascades. We use the Visual DSD simulation software in conjunction with localized reaction rates obtained from biophysical modeling to create chemical reaction networks of localized hybridization circuits that are then model checked using the PRISM model checking software.

We develop a DNA detection system employing the triggered self-assembly of a novel DNA dendritic nanostructure. Detection begins when a specific, single-stranded target DNA strand triggers a hybridization chain reaction between two distinct DNA hairpins. Each hairpin opens and hybridizes up to two copies of the other, and hence each layer of the growing dendritic nanostructure can in principle accommodate an exponentially increasing number of cognate molecules, generating a nanostructure with high molecular weight.

We build linear activatable assemblies employing a novel protection/deprotection strategy to strictly enforce the direction of tiling assembly growth to ensure the robustness of the assembly process. Our system consists of two tiles that can form a linear co-polymer. These tiles, which are initially protected such that they do not react with each other, can be activated to form linear co-polymers via the use of a strand displacing enzyme.

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.

The power of modern computer systems is due in no small part to their fantastic ability to adapt to whatever tasks they are charged with. Self-reconfigurable robots seek to provide that flexibility in hardware by building a system out of many individual modules, each with limited functionality, but with the ability to rearrange themselves to modify the shape and structure of the overall robotic system and meet whatever challenges are faced. Various hardware systems have been constructed for reconfigurable robots, and algorithms for them produce a wide variety of modes of locomotion. However, the task of efficiently controlling these complex systems -- possibly with thousands or millions of modules comprising a single robot -- is still not fully solved even after years of prior work on the topic.

In this thesis, we investigate the topic of theoretical control algorithms for lattice-style self-reconfigurable robots. These robots are composed of modules attached to each other in discrete lattice locations and only move by transitioning from one lattice location to another adjacent location. In our work, given the physical limitations of modules in a robot, we show a lower bound for the time to reconfiguration that robot. That is, transition the robot from one connected arrangement of modules to a different connected arrangement. Furthermore, we develop an algorithm with a running time that matches this lower bound both for a specific example reconfiguration problem and for general reconfiguration between any pair of 2D arrangements of modules. Since these algorithms match the demonstrated lower bound, they are optimal given the assumed abilities of the modules in the robot.

In addition to our theoretically optimal reconfiguration algorithms, we also make contributions to the more practical side of of this robotics field with a novel, physically stable control algorithm. The majority of prior theoretical work on control algorithms for self-reconfigurable robots did not consider the effects of gravity upon the robot. The result is that these algorithms often transform a robot into configurations -- arrangements of modules -- which are unstable and would likely break hardware on a real robot with thousands or millions of modules. In this thesis we present an algorithm for locomotion of a self-reconfigurable robot which is always physically stable in the presence of gravity even though we assume limited abilities for the robot's modules to withstand tension or sheer forces. This algorithm is highly scalable, able to be efficiently run on a robot with millions of modules, demonstrates significant speed advantages over prior scalable locomotion algorithms, and is resilient to errors in module actions or message passing. Overall, the contributions of this thesis extend both the theoretical and practical limits of what is possible with control algorithms for self-reconfigurable robots.

Self-assembly is a pervasive natural phenomenon that gives rise to complex structures and functions. It describes processes in which a disordered system of components form organized structures as a consequence of specific, local interactions among the components themselves, without any external direction. Biological self-assembled systems, evolved over billions of years, are more intricate, more energy efficient and more functional than anything researchers have currently achieved at the nanoscale. A challenge for human designed physical self-assembled systems is to catch up with mother nature. I argue through examples that DNA is an apt material to meet this challenge. This work presents:

1. 3D self-assembled DNA nanostructures.

2. Illustrations of the simplicity and power of toehold-mediated strand displacement interactions.

3. Algorithmic constructs in the tile assembly model.

Recently, solution-based systems for DNA computation have demonstrated the enormous potential of DNA nanosystems to do computation at the molecular-scale. These use DNA strands to encode values and use DNA hybridization reactions to perform computations. But most of these prior DNA computation systems relied on the diffusion of DNA strands to transport values during computations. During diffusion, DNA molecules randomly collide and interact in a three-dimensional fluidic space. At low concentrations and temperatures, diffusion can be quite slow and could impede the kinetics of these systems whereas at higher concentrations and temperature, unintended spurious interactions during diffusion can hinder the computations. Hence, increasing the concentration of DNA strands to speed up DNA hybridization reactions has the unfortunate side effect of increasing leaks, which are undesired hybridization reactions in the absence of input strands. Also, diffusion-based systems possess global states encoded via concentration of various species and hence exhibit only limited parallel ability.

To address these challenges, this dissertation describes a novel design for DNA computation called a localized hybridization network, where diffusion of DNA strands does not occur. Instead all of the DNA strands are localized by attaching them to an addressable substrate such as DNA nanotrack and DNA origami. This localization increases the relative concentration of the reacting DNA strands thereby speeding up the kinetics. This dissertation demonstrated a localized hybridization network that executed a chain reaction of five DNA hybridizations which executes faster than non-localized DNA reactions.

Another advantage of this approach is that each copy of the localized hybridization network operates independently of each other, allowing for a high level of parallelism. Localized hybridization networks also allow one to reuse the same DNA sequence to perform different actions at distinct location on the addressable substrate, increasing the scalability of such systems by exploiting the limited sequence space. An advantage of localized hybridization computational circuit is sharper switching behavior as information is encoded over the state of a single molecule. This also eliminates the need for thresholding as computation is performed locally eliminating the need for a global consensus.

There are many applications for localized hybridization networks. These include counting the number of disease marker molecules in a patient, detecting various cancer DNA sequences, and detecting and distinguishing bacteria by their distinguishing DNA. The results from localized DNA hybridization reactions may also be of practical use in performing surface computation on cellular membranes for disease detection and prevention.

Synthetic biology is an emerging technology field whose goal is to use biology as a substrate for engineering. Self-assembly is one of the many methods for fabricating such synthetic biosystems.

Self-assembly is a process where components spontaneously arrange themselves into organized aggregates by the selective affinity of substructures. DNA is an excellent candidate for making synthetic biological systems using self-assembly because of its modular structure and simple chemistry. This thesis describes several

techniques which use DNA as a nano-construction material and

explores the computational capabilities of DNA self-assembly.

For this dissertation, I set out to build a biomolecular computing device with several

useful properties, including compactness, robustness, high degrees of complexity, flexibility, scalability and easily characterized yields

and convergence rates. However, a unified device that satisfies all these properties is still many years away. Instead, this thesis presents designs, simulations,

and experimental results for several distinct DNA nano-systems, as

well as experimental protocols, each of which satisfies a subset of the above-mentioned properties. The hope is that the lessons learned from building all these biomolecular computational devices would bring us closer to our ultimate goal and would eventually pave the path for a computing device that satisfies all the properties. We experimentally demonstrate how we can reduce errors in tiling assembly using an optimized set of physical parameters. We propose a novel DNA tile design

which enforces directionality of growth, reducing assembly errors. We build simulation models to characterize damage in fragile nanostructures under the impact of various external forces. Furthermore, we investigate reversible computation as a means to provide self-repairability to such damaged structures.

We suggest two modifications of an existing DNA computing device,

called Whiplash PCR machine, which allow it to operate robustly outside of controlled laboratory conditions and allow it to implement a simple form of inter-device communication. We present analysis techniques which characterize the yields and time convergence of self-assembled DNA nanostructures. We also present an experimental demonstration of a novel DNA nanostructure which is capable of tiling the plane and could prove to be a way of building 3D DNA assemblies.

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.

For the past several decades, the discoveries, development, and improvement of microscale and nanoscale techniques and tools have been a major momentum driving the rapid technological advances across many areas of modern sciences and engineering, especially in multidisciplinary fields such as nucleic acid research. Owing to the remarkable programmability, long-term stability, and excellent coding capacity, nucleic acids have received much attention as a promising substrate for constructing next-generation data storage and computing systems at the molecular scale. These emerging applications bridge the knowledge from multiple disciplines including computer science, materials science, and biological science. Advances in nucleic acid technologies have also made significant contributions to life sciences and healthcare, leading to innovations that continue to improve disease prevention, diagnosis, and treatment. For example, techniques such as nucleic acid isothermal amplification hold great promises to enable widespread low-cost molecular testing of pathogens and infectious diseases without expensive instrumentation. The rapidly advancing field of nucleic acid research have given rise to a broad range of practical applications, many of which rely on the development and innovations of supporting technologies such as microscale and nanoscale fabrication techniques, which continue to help pave the way for designing and prototyping better and useful tools such as microfluidics and microarrays. Collectively, this dissertation focuses on these several distinct yet interconnected areas of research around nucleic acid technologies and contributes several novel techniques with applications spanning molecular computing, DNA data storage, molecular diagnostics, and benchtop microfabrication.

This dissertation is organized as follows. Chapter 1 presents an overview of recent advances in nucleic-acid-based molecular data storage and computing systems. DNA outperforms most conventional storage media in terms of information retention time, physical density, and volumetric coding capacity. Advances in synthesis and sequencing technologies have enabled implementations of large synthetic DNA storage with impressive storage capacity and reliable data recovery. Several robust DNA storage architectures featuring random access, error correction, and content-rewritability have been constructed with the potential for scalability and cost reduction. This chapter surveys these recent achievements and discusses alternative routes for overcoming the hurdles of engineering practical DNA storage systems. This chapter also reviews recent exciting work on in vivo DNA memory including intracellular recorders constructed by programmable genome editing tools. Besides information storage, DNA could serve as a versatile molecular computing substrate. Several state-of-the-art DNA computing techniques such as strand displacement, localized hybridization chain reactions, and enzymatic reaction networks are introduced. These simple primitives have facilitated rational designs and implementations of in vitro DNA reaction networks that emulate digital/analog circuits, artificial neural networks, or nonlinear dynamic systems. This chapter also highlights in vivo DNA computing modules such as CRISPR logic gates for building scalable genetic circuits in living cells. The chapter concludes with a discussion of applications and challenges of DNA-based data storage and computing and their far-reaching implications in biocomputing, security, and medicine.

Chapter 2 presents the design, modeling, and simulation of a novel molecular data storage architecture that enables highly efficient, scalable, multidimensional data organization and random access in large DNA storage systems. With impressive physical density and molecular-scale coding capacity, DNA is a promising substrate for building long-lasting data archival storage systems. To retrieve data from DNA storage, recent implementations typically rely on large libraries of meticulously designed orthogonal PCR primers, which fundamentally limit the capacity and scalability of practical DNA storage. This work combines nested and semi-nested PCR to enable multidimensional data organization and random access in large DNA storage. Our strategy effectively pushes the limit of DNA storage capacity and dramatically reduces the number of orthogonal primers needed for efficient PCR random access. Our design uses only k*n primers to uniquely address n^k data-encoding oligos. The architecture inherently supports various well-defined PCR random-access patterns that can be tailored to organize and preserve the underlying DNA-encoded data structures and relations in simple database-like formats such as rows, columns, tables, and blocks of data entries. We design in silico PCR experiments of a four-dimensional DNA storage to illustrate the mechanisms of sixteen different random-access patterns each requiring no more than two PCR reactions to selectively amplify a target dataset of various sizes. To better approximate the physical system, we formulate mathematical models based on empirical distributions to analyze the effect of pipetting, PCR bias, and PCR stochasticity on the performance of multidimensional data queries from large DNA storage.

Chapter 3 presents the design, modeling, and simulation of a renewable DNA logic computing scheme. An important achievement in the field of DNA computing has been the development of experimental protocols for evaluation of Boolean logic circuits. These protocols for DNA circuits generally take as inputs single-stranded DNA molecules that encode Boolean values, and via a series of DNA hybridization reactions then release ssDNA strands to indicate Boolean output values. However, most of these DNA circuits protocols are use-once only, and there remains the major challenge of designing DNA circuits to be renewable for use with multiple sets of inputs. Prior proposed schemes to make DNA gates renewable suffered from multiple problems, including waste accumulation, signal restoration, noise tolerance, and limited scalable complexity. In this work, we propose a scalable design and in silico demonstration of photoregulated renewable DNA seesaw logic circuits, which after processing a given set of inputs, can be repeatedly reset to reliably process other distinct inputs. To achieve renewability, specific toeholds in the system are labeled with photoresponsive molecules such as azobenzene to modulate the effective rate constants of toehold-mediated strand displacement (TMSD) reactions. Our proposed design strategy of leveraging the collective effect of TMSD and azobenzene-mediated dehybridization provides new perspectives on achieving synchronized and localized control of DNA hybridizations in complex and scalable reaction networks efficiently and economically. Various devices such as molecular walkers and motors could potentially be engineered reusable, be simulated and subsequently implemented using our simplified design strategy.

Chapter 4 present the design and experimental validation of a rapid, inexpensive, high-performance molecular home test kit for self-administered diagnosis of COVID-19 and other infectious diseases without instrumentation or trained personnel. To curb and monitor the spread of SARS-CoV-2, simple, affordable, accurate home tests are urgently needed for global population-scale surveillance. We report a rapid, low-cost (~2 USD), simple-to-use molecular test kit for self-administered at-home testing with high sensitivity and specificity. A one-pot lyophilization protocol was developed and optimized to preserve all required biochemical reagents of the test in a single microtube, facilitating long-term storage, inexpensive distribution, and simple testing without specialized instrumentation or trained personnel. The entire sample-to-answer workflow takes <60 minutes, including noninvasive sample collection, one-step RNA isolation, reverse-transcription loop-mediated isothermal amplification (RT-LAMP) in a thermos, and finally a direct visual inspection of colorimetric test result. Our test kit remains stable for ≥30 days at typical home-refrigeration temperature (4 °C) and ≥10 days at room temperature (~20 to 22 °C), achieving ≥95% analytical sensitivity and >99% specificity with a reproducible limit of detection down to at least five copies of viral RNA per microliter under both storage conditions. Notably, the lyophilized RT-LAMP assay demonstrated reduced false positives and enhanced tolerance to a wider range of incubation temperatures compared to conventional solution-based RT-LAMP reactions. Validation tests conducted using simulated SARS-CoV-2 infected samples confirmed rapid detection of SARS-CoV-2 virus from both anterior nasal swabs and gingival swabs. Our test successfully detected multiple SARS-CoV-2 variants and can be easily adapted to enable inexpensive near-patient/at-home molecular testing solutions for other pathogens and infectious diseases.

Chapter 5 presents the invention of a rapid, low-cost, benchtop microfabrication method termed UV-micropatterned miniaturization. Shrink lithography is a promising top-down micro/nanofabrication technique capable of miniaturizing patterns/structures to scales much smaller than the initial mold, however, rapid inexpensive fabrication of high-fidelity shrinkable microfeatures remains challenging. This work reports the discovery and characterization of a simple, fast, low-cost method for replicating and miniaturizing intricate micropatterns/structures on commodity heat-shrinkable polymers. Large-area permanent micropatterning on polystyrene and polyolefin shrink film is attained in one step under ambient conditions through brief irradiation by a shortwave UV pencil lamp. After baking briefly in an oven, the film shrinks biaxially and the miniaturized micropatterns emerge with significantly reduced surface area (up to 95%) and enhanced depth profile. The entire UV-micropatterned miniaturization process is highly reproducible and achievable on benchtop under a few minutes without chemicals or sophisticated apparatus. A variety of microgrid patterns are replicated and miniaturized with high yield and resolution on both planar and curved surfaces. Sequential UV exposures enable easy and rapid engineering of sophisticated microtopography with miniaturized, multi-scale, multi-dimensional microstructures. UV-ozone micropatterned polystyrene surfaces are well-suited for lab-on-a-chip analytical applications owing to the inherent biocompatibility and enhanced surface hydrophilicity. Miniaturization of dense, periodic micropatterns may facilitate low-cost prototyping of functional devices/surfaces such as micro-optics/sensors and tunable metamaterials.

DNA, the blueprint of life, is more than a carrier of genetic information. It offers a highly programmable substrate that can be used for computing, nanorobotics, and advanced imaging techniques. In this work, we use the programmable nature of synthetic DNA to engineer two novel applications. In the first part, DNA is programmed to improve the multiplexing capabilities of a fluorescence microscope while in the second part, we design a novel DNA computing architecture that uses a strand displacing polymerase enzyme. This thesis is a collection of 2 experimental papers, 2 theory papers, and 1 software paper. The general theme of this thesis is to exploit the programmable nature of DNA to develop new applications for the wider field of molecular biology, nanoimaging, and computer engineering.

Optical multiplexing is defined as the ability to study, detect, or quantify multiple objects of interest simultaneously. There are several ways to improve optical multiplexing, namely, using orthogonal wavelengths, multiple mesoscale geometries, orthogonal nucleic acid probes, or a combination of these. Most traditional techniques employ either the geometry or the color of single molecules to uniquely identify (or barcode) different species of interest. However, these techniques require complex sample preparation and multicolor hardware setup. In this work, we introduce a time-based amplification-free single-molecule barcoding technique using easy-to-design nucleic acid strands. A dye-labeled complementary reporter strand transiently binds to the programmed nucleic acid strands to emit temporal intensity signals. We program the DNA strands to emit uniquely identifiable temporal signals for molecular-scale fingerprinting. Since the reporters bind transiently to DNA devices, our method offers relative immunity to photobleaching. We use a single universal reporter strand for all DNA devices making our design extremely cost-effective. We show DNA strands can be programmed for generating a multitude of uniquely identifiable molecular barcodes. Our technique can be easily incorporated with the existing orthogonal methods that use wavelength or geometry to generate a large pool of distinguishable molecular barcodes thereby enhancing the overall multiplexing capabilities of single-molecule imaging. The proposed project has exciting transformative potential for nanoscale applications in fluorescence microscopy and cell biology since the development of temporal barcodes would allow for applications such as sensing miRNAs which are largely associated with disease diagnosis and therapeutics.

The regulation of cellular and molecular processes typically involves complex biochemical networks. Synthetic nucleic acid reaction networks (both enzyme-based and enzyme-free) can be systematically designed to approximate sophisticated biochemical processes. However, most of the prior experimental protocols for chemical reaction networks (CRNs) relied on either strand-displacement hybridization or restriction and exonuclease enzymatic reactions. These resulting synthetic systems usually suffer from either slow rates or leaky reactions. This work proposes an alternative architecture to implement arbitrary reaction networks, that is based entirely on strand-displacing polymerase reactions with nonoverlapping I/O sequences. First, the design for a simple protocol that can approximate arbitrary unimolecular and bimolecular reactions using polymerase strand displacement reactions is presented. Then these fundamental reaction systems are used as modules to show large-scale applications of the architecture, including an autocatalytic amplifier, a molecular-scale consensus protocol, and a dynamic oscillatory system. Finally, we engineer an \textit{in vitro} catalytic amplifier system as a proof-of-concept of our polymerase architecture since such sustainable amplifiers require careful sequence design and implementation.

Nucleic Acid hairpins have been a subject of study for the last four decades. They are composed of single strand that is

hybridized to itself, and the central section forming an unhybridized loop. In nature, they stabilize single stranded RNA, serve as nucleation

sites for RNA folding, protein recognition signals, mRNA localization and regulation of mRNA degradation. On the other hand,

DNA hairpins in biological contexts have been studied with respect to forming cruciform structures that can regulate gene expression.

The use of DNA hairpins as fuel for synthetic molecular devices, including locomotion, was proposed and experimental demonstrated in 2003. They

were interesting because they bring to the table an on-demand energy/information supply mechanism.

The energy/information is hidden (from hybridization) in the hairpin’s loop, until required.

The energy/information is harnessed by opening the stem region, and exposing the single stranded loop section.

The loop region is now free for possible hybridization and help move the system into a thermodynamically favourable state.

The hidden energy and information coupled with

programmability provides another functionality, of selectively choosing what reactions to hide and

what reactions to allow to proceed, that helps develop a topological sequence of events.

Hairpins have been utilized as a source of fuel for many different DNA devices. In this thesis, we program four different

molecular devices using DNA hairpins, and experimentally validate them in the

laboratory. 1) The first device: A

novel enzyme-free autocatalytic self-replicating system composed entirely of DNA that operates isothermally. 2) The second

device: Time-Responsive Circuits using DNA have two properties: a) asynchronous: the final output is always correct

regardless of differences in the arrival time of different inputs.

b) renewable circuits which can be used multiple times without major degradation of the gate motifs

(so if the inputs change over time, the DNA-based circuit can re-compute the output correctly based on the new inputs).

3) The third device: Activatable tiles are a theoretical extension to the Tile assembly model that enhances

its robustness by protecting the sticky sides of tiles until a tile is partially incorporated into a growing assembly.

4) The fourth device: Controlled Amplification of DNA catalytic system: a device such that the amplification

of the system does not run uncontrollably until the system runs out of fuel, but instead achieves a finite

amount of gain.

Nucleic acid circuits with the ability

to perform complex logic operations have many potential practical applications, for example the ability to achieve point of care diagnostics.

We discuss the designs of our DNA Hairpin molecular devices, the results we have obtained, and the challenges we have overcome

to make these truly functional.

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.