One dimensional boundaries for DNA tile self-assembly

In this paper we report the design and synthesis of DNA molecules (referred to as DNA tiles) with specific binding interactions that guide self-assembly to make one-dimensional assemblies shaped as lines, V's and X's. These DNA tile assemblies have been visualized by atomic force microscopy. The highly-variable distribution of shapes - e.g., the length of the arms of X-shaped assemblies - gives us insight into how the assembly process is occurring. Using stochastic models that simulate addition and dissociation of each type of DNA tile, as well as simplified models that more cleanly examine the generic phenomena, we dissect the contribution of accretion vs aggregation, reversible vs irreversible and seeded vs unseeded assumptions for describing the growth processes. The results suggest strategies for controlling self-assembly to make more uniformly-shaped assemblies

Pattern recognition in the nucleation kinetics of non-equilibrium self-assembly

Inspired by biology’s most sophisticated computer, the brain, neural networks constitute a profound reformulation of computational principles. Analogous high-dimensional, highly interconnected computational architectures also arise within information-processing molecular systems inside living cells, such as signal transduction cascades and genetic regulatory networks. Might collective modes analogous to neural computation be found more broadly in other physical and chemical processes, even those that ostensibly play non-information-processing roles? Here we examine nucleation during self-assembly of multicomponent structures, showing that high-dimensional patterns of concentrations can be discriminated and classified in a manner similar to neural network computation. Specifically, we design a set of 917 DNA tiles that can self-assemble in three alternative ways such that competitive nucleation depends sensitively on the extent of colocalization of high-concentration tiles within the three structures. The system was trained in silico to classify a set of 18 grayscale 30 × 30 pixel images into three categories. Experimentally, fluorescence and atomic force microscopy measurements during and after a 150 hour anneal established that all trained images were correctly classified, whereas a test set of image variations probed the robustness of the results. Although slow compared to previous biochemical neural networks, our approach is compact, robust and scalable. Our findings suggest that ubiquitous physical phenomena, such as nucleation, may hold powerful information-processing capabilities when they occur within high-dimensional multicomponent systems

Limits of economy and fidelity for programmable assembly of size-controlled triply-periodic polyhedra

We propose and investigate an extension of the Caspar-Klug symmetry principles for viral capsid assembly to the programmable assembly of size-controlled triply-periodic polyhedra, discrete variants of the Primitive, Diamond, and Gyroid cubic minimal surfaces. Inspired by a recent class of programmable DNA origami colloids, we demonstrate that the economy of design in these crystalline assemblies -- in terms of the growth of the number of distinct particle species required with the increased size-scale (e.g. periodicity) -- is comparable to viral shells. We further test the role of geometric specificity in these assemblies via dynamical assembly simulations, which show that conditions for simultaneously efficient and high-fidelity assembly require an intermediate degree of flexibility of local angles and lengths in programmed assembly. Off-target misassembly occurs via incorporation of a variant of disclination defects, generalized to the case of hyperbolic crystals. The possibility of these topological defects is a direct consequence of the very same symmetry principles that underlie the economical design, exposing a basic tradeoff between design economy and fidelity of programmable, size controlled assembly.Comment: 15 pages, 5 figures, 6 supporting movies (linked), Supporting Appendi

Rational design of self-assembly pathways for complex multicomponent structures.

The field of complex self-assembly is moving toward the design of multiparticle structures consisting of thousands of distinct building blocks. To exploit the potential benefits of structures with such "addressable complexity," we need to understand the factors that optimize the yield and the kinetics of self-assembly. Here we use a simple theoretical method to explain the key features responsible for the unexpected success of DNA-brick experiments, which are currently the only demonstration of reliable self-assembly with such a large number of components. Simulations confirm that our theory accurately predicts the narrow temperature window in which error-free assembly can occur. Even more strikingly, our theory predicts that correct assembly of the complete structure may require a time-dependent experimental protocol. Furthermore, we predict that low coordination numbers result in nonclassical nucleation behavior, which we find to be essential for achieving optimal nucleation kinetics under mild growth conditions. We also show that, rather surprisingly, the use of heterogeneous bond energies improves the nucleation kinetics and in fact appears to be necessary for assembling certain intricate 3D structures. This observation makes it possible to sculpt nucleation pathways by tuning the distribution of interaction strengths. These insights not only suggest how to improve the design of structures based on DNA bricks, but also point the way toward the creation of a much wider class of chemical or colloidal structures with addressable complexity.This work was carried out with support from the Eu- ropean Research Council (Advanced Grant 227758) and the Engineering and Physical Sciences Research Council Programme Grant EP/I001352/1. W.M.J. acknowledges support from the Gates Cambridge Trust and the Na- tional Science Foundation Graduate Research Fellowship under Grant No. DGE-1143678.This is the author accepted manuscript. The final version is available from PNAS at http://www.pnas.org/content/112/20/6313.abstract

Hierarchical assembly is more robust than egalitarian assembly in synthetic capsids

Self-assembly of complex and functional materials remains a grand challenge in soft material science. Efficient assembly depends on a delicate balance between thermodynamic and kinetic effects, requiring fine-tuning affinities and concentrations of subunits. By contrast, we introduce an assembly paradigm that allows large error-tolerance in the subunit affinity and helps avoid kinetic traps. Our combined experimental and computational approach uses a model system of triangular subunits programmed to assemble into T=3 icosahedral capsids comprising 60 units. The experimental platform uses DNA origami to create monodisperse colloids whose 3D geometry is controlled to nanometer precision, with two distinct bonds whose affinities are controlled to kBT precision, quantified in situ by static light scattering. The computational model uses a coarse-grained representation of subunits, short-ranged potentials, and Langevin dynamics. Experimental observations and modeling reveal that when the bond affinities are unequal, two distinct hierarchical assembly pathways occur, in which the subunits first form dimers in one case, and pentamers in another. These hierarchical pathways produce complete capsids faster and are more robust against affinity variation than egalitarian pathways, in which all binding sites have equal strengths. This finding suggests that hierarchical assembly may be a general engineering principle for optimizing self-assembly of complex target structures

Engineering DNA-Based Self-Assembly Systems to Produce Nanostructures and Chemical Patterns

While commonly known as a material that stores biological information essential for life, few realize that deoxyribonucleic acid (DNA) is also a wonderful building (i.e., physical structures) and computing material. The field of DNA nanotechnology aims to use DNA primarily to build and control matter on the nanoscale. In 2006, a technique known as DNA origami was developed, which allows for the formation of about any shape on the nanoscale. Such DNA origami have been used in many applications: nanodevices, nanotubes, nanoreactors. However, the small surface area of the origami often limits its usefulness. One promising method for building large (micron-sized) DNA origami structures is to self-assemble multiple origami components into well-defined structures. To date, however, such structures suffer low yields, long reaction times and require experimental optimization with no guiding principles. One primary reason is that a governing theory and experimental measurements behind such a self-assembly process are lacking. In this work, we develop coarse-grained computational simulations to describe and understand the self-assembly of finite-sized, multicomponent complexes (e.g., nine different DNA-origami components that form a square grid complex). To help inform the model, we experimentally investigate how various interface architectures between two self-assembling DNA origami components affect the reaction kinetics and thermodynamics. We further develop the accuracy of our simulations by incorporating these measurements and other thermodynamic measurements from our group and implement a computational algorithm that optimizes the interaction strengths between self-assembling components for reaction efficiency (i.e., speed and yield of the complex). With these experimentally-informed simulations, we suggest design improvements and provide yield predictions to an experimentally demonstrated tetrameric complex. Finally, with the overarching idea of using DNA-based components to self-assemble to produce ordered structures and patterns, we build a reaction-diffusion system whose reactions are programmed using DNA strand displacement and diffusion which occurs in a hydrogel, wherein patterns develop, and liquid reservoirs, which are used to supply the high energy components. With this reaction-diffusion system we create stable (i.e., unchanging in space and time) one and two-dimensional patterns of DNA molecules with millimeter-scale features

Bio-Nano Robo-Mofos : Design and Synthesis of DNA Origami Nanostructures and Assembly of Nanobot Superstructures

In the field of bio-nanotechnology, molecules like DNA are repurposed as building materials for the construction of self-assembling nanostructures. The DNA origami method involves rationally coding many short synthetic DNA strands which ‘fold’ longer scaffold strands into precise, addressable structures for applications in areas like medicine, structural biology and molecular biophysics. DNA origami subunits are also used to explore fundamental principles of self-assembly, revealing insights into biology and expanding our control of matter at the nanoscale. But despite the usefulness of the method, DNA origami designs are limited in size by the length of scaffold strands, and in scope by the available tools needed to navigate the complex geometries of DNA nanostructures. My thesis addresses this in two ways: First, I present a set of principles for the design of DNA origami nanotubes, a class of strained structure with many applications. I parametrised variables related to nanotube design and created a computational tool to convert desired geometries into DNA strand layouts. I validated this via synthesis of various designs, including novel nanotubes with pleated walls, reconfigurable twist and varying diameter, characterising them with TEM, SAXS and MD simulations. This revealed insights into how design variables affect properties such as diameter and rigidity, and how global strain affects DNA nanostructures. Next, I present two schemes for assembling DNA origami subunits into self-limiting, open superstructures, exploring fundamental principles to control self-assembly while also overcoming DNA origami’s size limitations. The first is a strain accumulation scheme, which was explored theoretically and then embodied in a modular subunit with allosteric binding domains. With simulation and synthesis, I demonstrated that the subunit could structurally encode the extent of its own polymerisation. The second scheme is Vernier assembly, in which I showed that the combined geometries of two DNA origami subunits could determine the size of a superstructure and explored parameters important to maximise yield. Both studies provide guidance for future studies and applications which may require finite superstructures made from small numbers of unique components. Combined, the works in this thesis expand the design space for DNA-nanotechnology and fields beyond, enabling a range of biologically-inspired nanoscale autonomous modular formations, or ‘Bio-Nano Robo-Mofos’

DESIGN AND APPLICATIONS OF DNA-BASED DEVICES FOR SELF-ASSEMBLY, MOLECULAR CIRCUITS, AND SOFT MATERIALS

Biologically inspired synthetic materials have led to novel technologies due of their ability to sense, influence, or adapt to their environment. One way to build these materials and devices is to utilize the high sequence specificity and innate biocompatibility of DNA. While once considered as a material useful for only storing genetic information, DNA-based devices are now being realized as molecular tools in fields such as therapeutics, diagnostics, regenerative medicine, and soft robotics. In this dissertation, we investigate the use of DNA to build programmable tools to control self-assembly, implement molecular computation, and direct material change processes. DNA origami nanostructures are useful tools for controlling the spatial patterns of proteins, nanoparticles, and fluorophores because they contain hundreds of independently functionalizable locations that can be engineered with nanoscale precision. However, the addressable surface area is currently limited by the size of single origami structures, and efficient, high-yield self-assembly of multiple origami into higher-order assemblies continues to be a challenge. To investigate the factors important for heterogeneous self-assembly of multiple origami, we experimentally measure the equilibrium distribution of four origami tiles in the monomer, intermediate, and final tetramer states as a function of temperature. We find that the thermodynamics of the self-assembly process is determined by the binding interface between origami. Simulations of the assembly kinetics suggest assembly occurs primarily via hierarchical pathways. Next, we engineer a DNA-based timer circuit that can be used in computational devices for molecular release or material control. The circuit releases target DNA sequences into solution at a programmable time with a tunable, constant rate. Multiple timer circuits can operate simultaneously, each releasing their target sequences at independent rates and times. We further develop the utility of the timer and similar DNA-based circuits as a means to control molecular events in biological environments, such as serum-supplemented cell media, where DNA-degrading nucleases can reduce the functional stability and lifetime of DNA-based devices. By implementing DNA circuit-protective design principles and by adding screening molecules to reduce nuclease activity, the functional lifetime of simple DNA circuits can be significantly increased. We develop a model by fitting parameters for reactions between nucleases and simple DNA circuits. Using the model, we can qualitatively predict the behavior of more complex circuits: multiple circuits in series and circuits containing competitive reactions. Finally, we investigate how DNA-based circuits can be used to trigger the high-degree swelling response of DNA-crosslinked metamorphic hydrogels. By coupling signal amplification to the triggering process, we demonstrate modular control over the timescale and degree of swelling. Further, we show control over the identity of the trigger molecule using molecular translators and computational controllers capable of converting complex chemical inputs into mechanical actuation

Materializing interaction

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, February 2013.Cataloged from PDF version of thesis. "September 2012."Includes bibliographical references (p. 141-148).At the boundary between people, objects and spaces, we encounter a broad range of surfaces. Their properties perform functional roles such as permeability, comfort or illumination, while conveying information such as an object's affordances, composition, or history of use. However, today surfaces are static and can neither adapt to our changing needs, nor communicate dynamic information and sense user input. As technology advances and we progress towards a world imbued with programmable materials, how will designers create physical surfaces that are adaptive and can take full advantage of our sensory apparatus? This dissertation looks at this question through the lens of a three-tier methodology consisting of the development of programmable composites; their application in design and architecture; and contextualization through a broader material and surface taxonomy. The focus is placed primarily on how materials and their aggregate surface properties can be used to engage our senses. A series of design probes and four final implementations are presented, each addressing specific programmable material and surface properties. Surflex, Sprout 1/O, and Shutters are continuous surfaces which can change shape to modify their topology, texture and permeability, and Six-Forty by Four-Eighty is a light-emitting display surface composed of autonomous and reconfigurable physical pixels. The technical and conceptual objectives of these designs are evaluated through exhibitions in a variety of public spaces, such as museums, galleries, fairs, as well as art and design festivals. This dissertation seeks to provide contributions on multiple levels, including: the development of techniques for the creation and control of programmable surfaces; the definition of a vocabulary and taxonomy to describe and compare previous work in this area; and finally, uncovering design principles for the underlying development of future programmable surface aesthetics.by Marcelo Coelho.Ph.D