DNA computing on surfaces

Endogenous enzyme-activated AND-gate DNA nanomachines for intracellular miRNA detection and cell-selective imaging

The occurrence and development of tumors are accompanied by the abnormal expression of specific microRNAs (miRNAs). Therefore, miRNAs are considered as an important biomarker. The establishment of efficient, simple and sensitive miRNA imaging methods in living cells will contribute to the early diagnosis, treatment and drug development of diseases. In this study, we developed an endogenous enzyme-initiated AND logic circuit using gold nanocubes (AuNCs) as carriers for simultaneous detection of miRNA-21 and miRNA-210 in cells. Apurinic/apyrimidinic endonuclease 1 (APE1) and telomerase (TE), which are overexpressed in cancer cells, act as control switches in a logic circuit that enables sensitive in situ analysis of intracellular miRNAs without additional external intervention. At the same time, due to the lack of necessary enzymes as activation switches, the DNA circuit in normal cells remains in an inactive state. This strategy effectively reduces the risk of false positive signal generation. Our research results show that the logic circuit can not only distinguish between cancer cells and normal cells, and able to distinguish between different types of cancer cells. This finding provides a promising approach to accurately identify cell types.

DNA Logical Computing on a Transistor for Cancer Molecular Diagnosis

Given the high degree of variability and complexity of cancer, precise monitoring and logical analysis of different nucleic acid markers are crucial for improving diagnostic precision and patient survival rates. However, existing molecular diagnostic methods normally suffer from high cost, cumbersome procedures, dependence on specialized equipment and the requirement of in-depth expertise in data analysis, failing to analyze multiple cancer-associated nucleic acid markers and provide immediate results in a point-of-care manner. Herein, we demonstrate a transistor-based DNA molecular computing (TDMC) platform that enables simultaneous detection and logical analysis of multiple microRNA (miRNA) markers on a single transistor. TDMC can perform not only basic logical operations such as "AND" and "OR", but also complex cascading computing, opening up new dimensions for multi-index logical analysis. Owing to the high efficiency, sensing and computations of multi-analytes can be operated on a transistor at a concentration as low as 2×10-16 M, reaching the lowest concentration for DNA molecular computing. Thus, TDMC achieves an accuracy of 98.4 % in the diagnosis of hepatocellular carcinoma from 62 serum samples. As a convenient and accurate platform, TDMC holds promise for applications in "one-stop" personalized medicine.

Rewireable Building Blocks for Enzyme-Powered DNA Computing Networks

Neural networks enable the processing of large, complex data sets with applications in disease diagnosis, cell profiling, and drug discovery. Beyond electronic computers, neural networks have been implemented using programmable biomolecules such as DNA; this confers unique advantages, such as greater portability, electricity-free operation, and direct analysis of patterns of biomolecules in solution. Analogous to bottlenecks in electronic computers, the computing power of DNA-based neural networks is limited by the ability to add more computing units, i.e., neurons. This limitation exists because current architectures require many nucleic acids to model a single neuron. Each additional neuron compounds existing problems such as long assembly times, high background signal, and cross-talk between components. Here, we test three strategies to solve this limitation and improve the scalability of DNA-based neural networks: (i) enzymatic synthesis for high-purity neurons, (ii) spatial patterning of neuron clusters based on their network position, and (iii) encoding neuron connectivity on a universal single-stranded DNA backbone. We show that neurons implemented via these strategies activate quickly, with a high signal-to-background ratio and process-weighted inputs. We rewired our modular neurons to demonstrate basic neural network motifs such as cascading, fan-in, and fan-out circuits. Finally, we designed a prototype two-layer microfluidic device to automate the operation of our circuits. We envision that our proposed design will help scale DNA-based neural networks due to its modularity, simplicity of synthesis, and compatibility with various neural network architectures. This will enable portable computing power for applications in portable diagnostics, compact data storage, and autonomous decision making for lab-on-a-chips.

Programmable Biomolecule-Mediated Processors

Programmable biomolecule-mediated computing is a new computing paradigm as compared to contemporary electronic computing. It employs nucleic acids and analogous biomolecular structures as information-storing and -processing substrates to tackle computational problems. It is of great significance to investigate the various issues of programmable biomolecule-mediated processors that are capable of automatically processing, storing, and displaying information. This Perspective provides several conceptual designs of programmable biomolecule-mediated processors and provides some insights into potential future research directions for programmable biomolecule-mediated processors.

A molecular assessment of the practical potential of DNA-based computation

The immense information density of DNA and its potential for massively parallelized computations, paired with rapidly expanding data production and storage needs, have fueled a renewed interest in DNA-based computation. Since the construction of the first DNA computing systems in the 1990s, the field has grown to encompass a diverse array of configurations. Simple enzymatic and hybridization reactions to solve small combinatorial problems transitioned to synthetic circuits mimicking gene regulatory networks and DNA-only logic circuits based on strand displacement cascades. These have formed the foundations of neural networks and diagnostic tools that aim to bring molecular computation to practical scales and applications. Considering these great leaps in system complexity as well as in the tools and technologies enabling them, a reassessment of the potential of such DNA computing systems is warranted.

Using Single-Molecule FRET to Evaluate DNA Nanodevices at Work

The observation of DNA nanodevices at a single molecule (i.e., device) level and in real time provides rich information that is typically masked in ensemble measurements. Single-molecule fluorescence resonance energy transfer (smFRET) offers a means to directly follow dynamic conformational or compositional changes that DNA nanodevices undergo while operating, thereby retrieving insights critical for refining them toward optimal function. To be successful, smFRET measurements require careful execution and meticulous data analysis for robust statistics. Here we outline the elemental steps for smFRET experiments on DNA nanodevices, starting from microscope slide preparation for single-molecule observation to data acquisition and analysis.

DNA strand displacement based computational systems and their applications

DNA computing has become the focus of computing research due to its excellent parallel processing capability, data storage capacity, and low energy consumption characteristics. DNA computational units can be precisely programmed through the sequence specificity and base pair principle. Then, computational units can be cascaded and integrated to form large DNA computing systems. Among them, DNA strand displacement (DSD) is the simplest but most efficient method for constructing DNA computing systems. The inputs and outputs of DSD are signal strands that can be transferred to the next unit. DSD has been used to construct logic gates, integrated circuits, artificial neural networks, etc. This review introduced the recent development of DSD-based computational systems and their applications. Some DSD-related tools and issues are also discussed.

An automated DNA computing platform for rapid etiological diagnostics

Rapid and accurate classification of the etiology for acute respiratory illness not only helps establish timely therapeutic plans but also prevents inappropriate use of antibiotics. Host gene expression patterns in peripheral blood can discriminate bacterial from viral causes of acute respiratory infection (ARI) but suffer from long turnaround time, as well as high cost resulting from the measurement methods of microarrays and next-generation sequencing. Here, we developed an automated DNA computing-based platform that can implement an in silico trained classification model at the molecular level with seven different mRNA expression patterns for accurate diagnosis of ARI etiology in 4 hours. By integrating sample loading, marker amplification, classifier implementation, and results reporting into one platform, we obtained a diagnostic accuracy of 87% in 80 clinical samples without the aid of computer and laboratory technicians. This platform creates opportunities toward an accurate, rapid, low-cost, and automated diagnosis of disease etiology in emergency departments or point-of-care clinics.

Intelligent Programmable DNA Nanomachines for the Spatially Controllable Imaging of Intracellular MicroRNA

The high programmability of DNA molecules makes them particularly suitable for constructing artificial molecular machines to perform sophisticated functions by simulating complex living systems. However, intelligent DNA nanomachines which can perform precise tasks logically in complex environments still remain challenging. Herein, we develop a general strategy to design a pH-responsive programmable DNA (PRPD) nanomachine to perform multilayer DNA cascades, enabling precise sensing and calculation of intracellular biomolecules. The PRPD nanomachine is built on a four-stranded DNAzyme walker precursor with a DNA switch on the surface of an Au nanoparticle, which is capable of precisely responding to pH variations in living cells by sequence tuning. This multilayer DNA cascade networks have been applicated in spatially controlled imaging of intracellular microRNA, which efficiently avoided the DNA nanomachine activated by nonspecific extracellular molecules and achieved apparent signal amplification. Our strategy enables the sensing-computing-output functional integration of DNA nanomachines, facilitating the application of programmable and complex nanomachines in nanoengineering, chemistry, and biomedicine.

Graph Computation Using Algorithmic Self-Assembly of DNA Molecules

DNA molecules have been used as novel computing tools, by which Synthetic DNA was designed to execute computing processes with a programmable sequence. Here, we proposed a parallel computing method using DNA origamis as agents to solve the three-color problem, an example of the graph problem. Each agent was fabricated with a DNA origami of ∼50 nm diameter and contained DNA probes with programmable sticky ends that execute preset computing processes. With the interaction of different nanoagents, DNA molecules self-assemble into spatial nanostructures, which embody the computation results of the three-color problem with polynomial numbers of computing nanoagents in a one-pot annealing step. The computing results were confirmed by atomic force microscopy. Our method is completely different from existing DNA computing methods in its computing algorithm, and it has an advantage in terms of computational complexity and results detection for solving graph problems.

Logical Analysis of Multiple Single-Nucleotide-Polymorphisms with Programmable DNA Molecular Computation for Clinical Diagnostics

Analyzing complex single-nucleotide-polymorphism (SNP) combinations in the genome is important for research and clinical applications, given that different SNP combinations can generate different phenotypic consequences. Recent works have shown that DNA-based molecular computing is powerful for simultaneously sensing and analyzing complex molecular information. Here, we designed a switching circuit-based DNA computational scheme that can integrate the sensing of multiple SNPs and simultaneously perform logical analysis of the detected SNP information to directly report clinical outcomes. As a demonstration, we successfully achieved automatic and accurate identification of 21 different blood group genotypes from 83 clinical blood samples with 100 % accuracy compared to sequencing data in a more rapid manner (3 hours). Our method enables a new mode of automatic and logical sensing and analyzing subtle molecular information for clinical diagnosis, as well as guiding personalized medication.

An RNA-Based Theory of Natural Universal Computation

Life is confronted with computation problems in a variety of domains including animal behavior, single-cell behavior, and embryonic development. Yet we currently do not know of a naturally existing biological system that is capable of universal computation, i.e., Turing-equivalent in scope. Generic finite-dimensional dynamical systems (which encompass most models of neural networks, intracellular signaling cascades, and gene regulatory networks) fall short of universal computation, but are assumed to be capable of explaining cognition and development. I present a class of models that bridge two concepts from distant fields: combinatory logic (or, equivalently, lambda calculus) and RNA molecular biology. A set of basic RNA editing rules can make it possible to compute any computable function with identical algorithmic complexity to that of Turing machines. The models do not assume extraordinarily complex molecular machinery or any processes that radically differ from what we already know to occur in cells. Distinct independent enzymes can mediate each of the rules and RNA molecules solve the problem of parenthesis matching through their secondary structure. In the most plausible of these models all of the editing rules can be implemented with merely cleavage and ligation operations at fixed positions relative to predefined motifs. This demonstrates that universal computation is well within the reach of molecular biology. It is therefore reasonable to assume that life has evolved - or possibly began with - a universal computer that yet remains to be discovered. The variety of seemingly unrelated computational problems across many scales can potentially be solved using the same RNA-based computation system. Experimental validation of this theory may immensely impact our understanding of memory, cognition, development, disease, evolution, and the early stages of life.

Programmable and scalable assembly of a flexible hexagonal DNA origami

Nanoscale structures demonstrate considerable potential utility in the construction of nanorobots, nanomachines, and many other devices. In this study, a hexagonal DNA origami ring was assembled and visualized via atomic force microscopy. The DNA origami shape could be programmed into either a hexagonal or linear shape with an open or folded pattern. The flexible origami was robust and switchable for dynamic pattern recognition. Its edges were folded by six bundles of DNA helices, which could be opened or folded in a honeycomb shape. Additionally, the edges were programmed into a concave-convex pattern, which enabled linkage between the origami and dipolymers. Furthermore, biotin-streptavidin labels were embedded at each edge for nanoscale calibration. The atomic force microscopy results demonstrated the stability and high-yield of the flexible DNA origami ring. The polymorphous nanostructure is useful for dynamic nano-construction and calibration of structural probes or sensors.

X-ray photoemission studies of the interaction of metals and metal ions with DNA

X-ray Photoelectron Spectroscopy (XPS) has been used to study the interactions of heavy metal ions with DNA with some success. Surface sensitivity and selectivity of XPS are advantageous for identifying and characterizing the chemical and elemental structure of the DNA to metal interaction. This review summarizes the status of what amounts to a large part of the photoemission investigations of biomolecule interactions with metals and offers insight into the mechanism for heavy metal-bio interface interactions. Specifically, it is seen that metal interaction with DNA results in conformational changes in the DNA structure.

Aggregation Properties of Albumin in Interacting with Magnetic Fluids

In this study, interactions of Fe3O4 magnetic nanoparticles with serum albumin biomolecules in aqueous solutions were considered. The studies were conducted with the laser correlation spectroscopy and optical analysis of dehydrated films. It was shown that the addition of magnetite to an albumin solution at low concentrations of up to 10-6 g/L led to the formation of aggregates with sizes of up to 300 nm in the liquid phase and an increase in the number of spiral structures in the dehydrated films, which indicated an increase in their stability. With a further increase in the magnetite concentration in the solution (from 10-4 g/L), the magnetic particles stuck together and to albumin, thus forming aggregates with sizes larger than 1000 nm. At the same time, the formation of morphological structures in molecular films was disturbed, and a characteristic decrease in their stability occurred. Most stable films were formed at low concentrations of magnetic nanoparticles (less than 10-4 g/L) when small albumin-magnetic nanoparticle aggregates were formed. These results are important for characterizing the interaction processes of biomolecules with magnetic nanoparticles and can be useful for predicting the stability of biomolecular films with the inclusion of magnetite particles.

Solving the Family Traveling Salesperson Problem in the Adleman-Lipton model based on DNA computing

The Family Traveling Salesperson Problem (FTSP) is a variant of the Traveling Salesperson Problem (TSP), in which all vertices are divided into several different families, and the goal of the problem is to find a loop that concatenates a specified number of vertices with minimal loop overhead. As a Non-deterministic Polynomial Complete (NP-complete) problem, it is difficult to deal with it by the traditional computing. On the contrary, as a computer with strong parallel ability, the DNA computer has incomparable advantages over digital computers when dealing with NP problems. Based on this, a DNA algorithm is proposed to deal with FTSP based on the Adleman-Lipton model. In the algorithm, the solution of the problem can be obtained by executing several basic biological manipulations on DNA molecules with O(N2) computing complexity (N is the number of vertices in the problem without the origin). Through the simulation experiments on some benchmark instances, the results show that the parallel DNA algorithm has better performance than traditional computing. The effectiveness of the algorithm is verified by deducing the algorithm process in detail. Furthermore, the algorithm further proves that DNA computing, as one of the parallel computing methods, has the potential to solve more complex big data problems.

Programmable DNA-Based Boolean Logic Microfluidic Processing Unit

As molecular computing materials, information-encoded deoxyribonucleic acid (DNA) strands provide a logical computing process by cascaded and parallel chain reactions. However, the reactions in DNA-based combinational logic computing are mostly achieved through a manual process by adding desired DNA molecules in a single microtube or a substrate. For DNA-based Boolean logic, using microfluidic chips can afford automated operation, programmable control, and seamless combinational logic operation, similar to electronic microprocessors. In this paper, we present a programmable DNA-based microfluidic processing unit (MPU) chip that can be controlled via a personal computer for performing DNA calculations. To fabricate this DNA-based MPU, polydimethylsiloxane was cast using double-sided molding techniques for alignment between the microfluidics and valve switch. For a uniform surface, molds fabricated using a three-dimensional printer were spin-coated by a polymer. For programming control, the valve switch arms were operated by servo motors. In the MPU controlled via a personal computer or smartphone application, the molecules with two input DNAs and a logic template DNA were reacted for the basic AND and OR operations. Furthermore, the DNA molecules reacted in a cascading manner for combinational AND and OR operations. Finally, we demonstrated a 2-to-1 multiplexer and the XOR operation with a three-step cascade reaction using the simple DNA-based MPU, which can perform Boolean logic operations (AND, OR, and NOT). Through logic combination, this DNA-based Boolean logic MPU, which can be operated using programming language, is expected to facilitate the development of complex functional circuits such as arithmetic logical units and neuromorphic circuits.

The Construction of DNA Logic Gates Restricted to Certain Live Cells Based on the Structure Programmability and Aptamer-Cell Affinity of G-Quadruplexes

DNA computation is considered a fascinating alternative to silicon-based computers; it has evoked substantial attention and made rapid advances. Besides realizing versatile functions, implementing spatiotemporal control of logic operations, especially at the cellular level, is also of great significance to the development of DNA computation. However, developing simple and efficient methods to restrict DNA logic gates performing in live cells is still a challenge. In this work, a series of DNA logic gates was designed by taking full advantage of the diversity and programmability of the G-quadruplex (G4) structure. More importantly, by further using the high affinity and specific endocytosis of cells to aptamer G4, an INHIBIT logic gate has been realized whose operational site is precisely restricted to specific live cells. The design strategy might have great potential in the field of molecular computation and smart bio-applications.

Programmable Aggregation of Artificial Cells with DNA Signals

Cell aggregation is a complex behavior that is closely related to the viability, differentiation, and migration of cells. An effort to create synthetic analogs could lead to considerable advances in cell physiology and biophysics. Rendering and modulating such a dynamic artificial cell system require mechanisms for receiving, transducing, and transmitting intercellular signals, yet effective tools are limited at present. Here we construct synthetic cells from engineered lipids and show their programmable aggregation behaviors using DNA oligonucleotides as signaling molecules. The artificial cells have transmembrane channels made of DNA origami that are used to recognize and process intercellular signals. We demonstrate that multiple small vesicles aggregate onto a giant vesicle after a transduction of external DNA signals by an intracellular enzyme and that the small vesicles dissociate when receiving "release" signals. This work provides new possibilities for building synthetic protocells capable of chemical communication and coordination.

The art of molecular computing: Whence and whither

An astonishingly diverse biomolecular circuitry orchestrates the functioning machinery underlying every living cell. These biomolecules and their circuits have been engineered not only for various industrial applications but also to perform other atypical functions that they were not evolved for-including computation. Various kinds of computational challenges, such as solving NP-complete problems with many variables, logical computation, neural network operations, and cryptography, have all been attempted through this unconventional computing paradigm. In this review, we highlight key experiments across three different ''eras'' of molecular computation, beginning with molecular solutions, transitioning to logic circuits and ultimately, more complex molecular networks. We also discuss a variety of applications of molecular computation, from solving NP-hard problems to self-assembled nanostructures for delivering molecules, and provide a glimpse into the exciting potential that molecular computing holds for the future. Also see the video abstract here: https://youtu.be/9Mw0K0vCSQw.

Toward Smart Information Processing with Synthetic DNA Molecules

DNA, a biological macromolecule, is a naturally evolved information material. From the structural point of view, an individual DNA strand can be considered as a chain of data with its bases working as single units. For decades, due to the high biochemical stability, large information storage capacity, and high recognition specificity, DNA has been recognized as an attractive material for information processing. Especially, the chemical synthesis strategies and DNA sequencing techniques have been rapidly developed recently, further enabling encoding information with synthetic DNA molecules. Herein, recent progresses are summarized on information processing based on synthetic DNA molecules from three aspects including information storage, computation, and encryption, and proposed the challenges and future development directions.

Nanopore decoding for a Hamiltonian path problem

DNA computing has attracted attention as a tool for solving mathematical problems due to the potential for massive parallelism with low energy consumption. However, decoding the output information to a human-recognizable signal is generally time-consuming owing to the requirement for multiple steps of biological operations. Here, we describe simple and rapid decoding of the DNA-computed output for a directed Hamiltonian path problem (HPP) using nanopore technology. In this approach, the output DNA duplex undergoes unzipping whilst passing through an α-hemolysin nanopore, with information electrically decoded as the unzipping time of the hybridized strands. As a proof of concept, we demonstrate nanopore decoding of the HPP of a small graph encoded in DNA. Our results show the feasibility of nanopore measurement as a rapid and label-free decoding method for mathematical DNA computation using parallel self-assembly.

A DNA algorithm for the job shop scheduling problem based on the Adleman-Lipton model

A DNA (DeoxyriboNucleic Acid) algorithm is proposed to solve the job shop scheduling problem. An encoding scheme for the problem is developed and DNA computing operations are proposed for the algorithm. After an initial solution is constructed, all possible solutions are generated. DNA computing operations are then used to find an optimal schedule. The DNA algorithm is proved to have an O(n2) complexity and the length of the final strand of the optimal schedule is within appropriate range. Experiment with 58 benchmark instances show that the proposed DNA algorithm outperforms other comparative heuristics.

DNA Computing Model for Satisfiability Problem Based on Hybridization Chain Reaction

Satisfiability problem is a famous nondeterministic polynomial-time complete (NP-complete) problem, which has always been a hotspot in artificial intelligence. In this paper, by combining the advantages of DNA origami with hybridization chain reaction, a computing model was proposed to solve the satisfiability problem. For each clause in the given formula, a DNA origami device was devised. The device corresponding to the clause was capable of searching for assignments that satisfied the clause. When all devices completed the search in parallel, the intersection of these satisfying assignments found must satisfy all the clauses. Therefore, whether the given formula is satisfiable or not was decided. The simulation results demonstrated that the proposed computing model was feasible. Our work showed the capability of DNA origami in architecting automatic computing device. The paper proposed a novel method for designing functional nanoscale devices based on DNA origami.

Encoding scheme for data storage and retrieval on DNA computers

There has been exponential growth in the amount of data being generated on a daily basis. Such a huge amount of data creates a need for efficient data storage techniques. Due to the limitations of existing storage media, new storage solutions have always been of interest. There have been recent developments in order to efficiently use synthetic deoxyribonucleic acid (DNA) for information storage. DNA storage has attracted researchers because of its extremely high data storage density, about 1 exabyte/mm3 and long life under easily achievable conditions. This work presents an encoding scheme for DNA-based data storage system with controllable redundancy and reliability, the authors have also talked about the feasibility of the proposed method. The authors have also analysed the proposed algorithm for time and space complexity. The proposed encoding scheme tries to minimise the bases per letter ratio while controlling the redundancy. They have experimented with three different types of data with a value of redundancy as 0.75. In the randomised simulation setup, it was observed that the proposed algorithm was able to correctly retrieve the stored data in our experiments about 94% of the time. In the situation, where redundancy was increased to 1, the authors were able to retrieve all the information correctly in the proposed experiments.

Cancer diagnosis with DNA molecular computation

Early and precise cancer diagnosis substantially improves patient survival. Recent work has revealed that the levels of multiple microRNAs in serum are informative as biomarkers for the diagnosis of cancers. Here, we designed a DNA molecular computation platform for the analysis of miRNA profiles in clinical serum samples. A computational classifier is first trained in silico using miRNA profiles from The Cancer Genome Atlas. This is followed by a computationally powerful but simple molecular implementation scheme using DNA, as well as an effective in situ amplification and transformation method for miRNA enrichment in serum without perturbing the original variety and quantity information. We successfully achieved rapid and accurate cancer diagnosis using clinical serum samples from 22 healthy people (8) and people with lung cancer (14) with an accuracy of 86.4%. We envision that this DNA computational platform will inspire more clinical applications towards inexpensive, non-invasive and rapid disease screening, classification and progress monitoring.

A new DNA-based model for finite field arithmetic

A Galois fieldG F ( p n ) withp ≥ 2 a prime number and n ≥ 1 is a mathematical structure widely used in Cryptography and Error Correcting Codes Theory. In this paper, we propose a novel DNA-based model for arithmetic overG F ( p n ) . Our model has three main advantages over other previously described models. First, it has a flexible implementation in the laboratory that allows the realization arithmetic calculations in parallel forp ≥ 2 , while the tile assembly and the sticker models are limited to p = 2 . Second, the proposed model is less prone to error, because it is grounded on conventional Polymerase Chain Reaction (PCR) amplification and gel electrophoresis techniques. Hence, the problems associated to models such as tile-assembly and stickers, that arise when using more complex molecular techniques, such as hybridization and denaturation, are avoided. Third, it is simple to implement and requires 50 ng/μL per DNA double fragment used to develop the calculations, since the only feature of interest is the size of the DNA double strand fragments. The efficiency of our model has execution times of orderO ( 1 ) andO ( n ) , for the addition and multiplication overG F ( p n ) , respectively. Furthermore, this paper provides one of the few experimental evidences of arithmetic calculations for molecular computing and validates the technical applicability of the proposed model to perform arithmetic operations over G F ( p n ) .

Fabrication of Electrochemical-Based Bioelectronic Device and Biosensor Composed of Biomaterial-Nanomaterial Hybrid

The field of bioelectronics has paved the way for the development of biochips, biomedical devices, biosensors and biocomputation devices. Various biosensors and biomedical devices have been developed to commercialize laboratory products and transform them into industry products in the clinical, pharmaceutical, environmental fields. Recently, the electrochemical bioelectronic devices that mimicked the functionality of living organisms in nature were applied to the use of bioelectronics device and biosensors. In particular, the electrochemical-based bioelectronic devices and biosensors composed of biomolecule-nanoparticle hybrids have been proposed to generate new functionality as alternatives to silicon-based electronic computation devices, such as information storage, process, computations and detection. In this chapter, we described the recent progress of bioelectronic devices and biosensors based on biomaterial-nanomaterial hybrid.

Dynamic colloidal nanoparticle assembly triggered by aptamer-receptor interactions on live cell membranes

Cells use dynamic systems such as enzyme cascades and signaling networks to control cellular functions. Synthetic dynamic systems that can be target-responsive have great potential to be applied for biomedical applications but the operation of such dynamic systems in complex cellular environments remains challenging. Here, we engineered an aptamer and DNA displacement reaction-based dynamic system that can transform its nanostructure in response to the epithelial cell adhesion molecule (EpCAM) on live cell membranes. The dynamic system consisted of a core nanoparticle and small satellite nanoparticles. With the modifications of different DNA hairpin strands and swing arm strands partially hybridized with an aptamer that specifically recognizes the EpCAM, the two separated particles can dynamically assemble into a core-satellite assembly by aptamer-receptor interactions on the cell membrane surface. The structural change of the system from separated particles to a core-satellite assembly generated plasmonic coupled hot spots for surface-enhanced Raman scattering (SERS) for sensitively capturing the dynamic structural change of the nanoassembly in the cellular environment. These concepts provide strategies for engineering dynamic nanotechnology systems for biological and biomedical applications in complex biological environments.

A study on a special DNA nanotube assembled from two single-stranded tiles

Single-stranded tile (SST) strategy offers precise control over the circumferences of nanotubes while the kinetic trap in the process of the self-assembly prevents the formation of wider tubes. Here, we report a simple and efficient method to build DNA nanotubes using only 2 SSTs via one-pot annealing. The diameters of the 2-SST nanotubes were much larger than what the kinetic trap theory would predict, indicating a new mechanism was at play in the formation of these nanotubes. Further investigation suggested that the 2-SST nanotubes were assembled through a hierarchical pathway that involved an intermediate formation of 2-SST nano-lines.

All-Optical Reinforcement Learning In Solitonic X-Junctions

Ethology has shown that animal groups or colonies can perform complex calculation distributing simple decision-making processes to the group members. For example ant colonies can optimize the trajectories towards the food by performing both a reinforcement (or a cancellation) of the pheromone traces and a switch from one path to another with stronger pheromone. Such ant’s processes can be implemented in a photonic hardware to reproduce stigmergic signal processing. We present innovative, completely integrated X-junctions realized using solitonic waveguides which can provide both ant’s decision-making processes. The proposed X-junctions can switch from symmetric (50/50) to asymmetric behaviors (80/20) using optical feedbacks, vanishing unused output channels or reinforcing the used ones.

Genetic programs can be compressed and autonomously decompressed in live cells

Fundamental computer science concepts have inspired novel information-processing molecular systems in test tubes 1-13 and genetically encoded circuits in live cells 14-21 . Recent research has shown that digital information storage in DNA, implemented using deep sequencing and conventional software, can approach the maximum Shannon information capacity 22 of two bits per nucleotide 23 . In nature, DNA is used to store genetic programs, but the information content of the encoding rarely approaches this maximum 24 . We hypothesize that the biological function of a genetic program can be preserved while reducing the length of its DNA encoding and increasing the information content per nucleotide. Here we support this hypothesis by describing an experimental procedure for compressing a genetic program and its subsequent autonomous decompression and execution in human cells. As a test-bed we choose an RNAi cell classifier circuit 25 that comprises redundant DNA sequences and is therefore amenable for compression, as are many other complex gene circuits 15,18,26-28 . In one example, we implement a compressed encoding of a ten-gene four-input AND gate circuit using only four genetic constructs. The compression principles applied to gene circuits can enable fitting complex genetic programs into DNA delivery vehicles with limited cargo capacity, and storing compressed and biologically inert programs in vivo for on-demand activation.

Microbial nanowires - Electron transport and the role of synthetic analogues

Electron transfer is central to cellular life, from photosynthesis to respiration. In the case of anaerobic respiration, some microbes have extracellular appendages that can be utilised to transport electrons over great distances. Two model organisms heavily studied in this arena are Shewanella oneidensis and Geobacter sulfurreducens. There is some debate over how, in particular, the Geobacter sulfurreducens nanowires (formed from pilin nanofilaments) are capable of achieving the impressive feats of natural conductivity that they display. In this article, we outline the mechanisms of electron transfer through delocalised electron transport, quantum tunnelling, and hopping as they pertain to biomaterials. These are described along with existing examples of the different types of conductivity observed in natural systems such as DNA and proteins in order to provide context for understanding the complexities involved in studying the electron transport properties of these unique nanowires. We then introduce some synthetic analogues, made using peptides, which may assist in resolving this debate. Microbial nanowires and the synthetic analogues thereof are of particular interest, not just for biogeochemistry, but also for the exciting potential bioelectronic and clinical applications as covered in the final section of the review.

STATEMENT OF SIGNIFICANCE

Some microbes have extracellular appendages that transport electrons over vast distances in order to respire, such as the dissimilatory metal-reducing bacteria Geobacter sulfurreducens. There is significant debate over how G. sulfurreducens nanowires are capable of achieving the impressive feats of natural conductivity that they display: This mechanism is a fundamental scientific challenge, with important environmental and technological implications. Through outlining the techniques and outcomes of investigations into the mechanisms of such protein-based nanofibrils, we provide a platform for the general study of the electronic properties of biomaterials. The implications are broad-reaching, with fundamental investigations into electron transfer processes in natural and biomimetic materials underway. From these studies, applications in the medical, energy, and IT industries can be developed utilising bioelectronics.

Constructing a Multiplexed DNA Pattern by Combining Precise Magnetic Manipulation and DNA-Driven Assembly

There is an urgent demand to construct multiplexed biomolecular patterns to obtain more biological information from a single experiment. However, with only limited reports focusing on defective top-down approaches, challenges remain to develop a bottom-up strategy for multiplexed patterning. To this end, a novel strategy has been proposed to fabricate multiplexed DNA patterns via macroscopic assembly through combined precise magnetic manipulation and DNA hybridization-driven self-assembly. Therefore, a multiplexed DNA pattern composed of glass fibers loaded with multiple specific strands of DNA was constructed, and its potential application in simultaneous detection of multiplex target DNA was demonstrated. Moreover, the fabricated multiplexed DNA pattern shows an erasable behavior because the hybridized DNA can be disassembled by strand displacement.

Optical NP problem solver on laser-written waveguide platform

Cognitive photonic networks are researched to efficiently solve computationally hard problems. Flexible fabrication techniques for the implementation of such networks into compact and scalable chips are desirable for the study of new optical computing schemes and algorithm optimization. Here we demonstrate a femtosecond laser-written optical oracle based on cascaded directional couplers in glass, for the solution of the Hamiltonian path problem. By interrogating the integrated photonic chip with ultrashort laser pulses, we were able to distinguish the different paths traveled by light pulses, and thus infer the existence or the absence of the Hamiltonian path in the network by using an optical correlator. This work proves that graph theory problems may be easily implemented in integrated photonic networks, down scaling the net size and speeding up execution times.

DNA multi-bit non-volatile memory and bit-shifting operations using addressable electrode arrays and electric field-induced hybridization

DNA has been employed to either store digital information or to perform parallel molecular computing. Relatively unexplored is the ability to combine DNA-based memory and logical operations in a single platform. Here, we show a DNA tri-level cell non-volatile memory system capable of parallel random-access writing of memory and bit shifting operations. A microchip with an array of individually addressable electrodes was employed to enable random access of the memory cells using electric fields. Three segments on a DNA template molecule were used to encode three data bits. Rapid writing of data bits was enabled by electric field-induced hybridization of fluorescently labeled complementary probes and the data bits were read by fluorescence imaging. We demonstrated the rapid parallel writing and reading of 8 (2³) combinations of 3-bit memory data and bit shifting operations by electric field-induced strand displacement. Our system may find potential applications in DNA-based memory and computations.

DNA based technology holds promise for non-volatile memory and computational tasks, yet the relatively slow hybridization kinetics remain a bottleneck. Here, Song et al. have developed an electric field-induced hybridization platform that can speed up multi-bit memory and logic operations.

Engineering nucleic acid structures for programmable molecular circuitry and intracellular biocomputation

Nucleic acids have attracted widespread attention due to the simplicity with which they can be designed to form discrete structures and programmed to perform specific functions at the nanoscale. The advantages of DNA/RNA nanotechnology offer numerous opportunities for in-cell and in-vivo applications, and the technology holds great promise to advance the growing field of synthetic biology. Many elegant examples have revealed the potential in integrating nucleic acid nanostructures in cells and in vivo where they can perform important physiological functions. In this Review, we summarize the current abilities of DNA/RNA nanotechnology to realize applications in live cells and then discuss the key problems that must be solved to fully exploit the useful properties of nanostructures. Finally, we provide viewpoints on how to integrate the tools provided by DNA/RNA nanotechnology and related new technologies to construct nucleic acid nanostructure-based molecular circuitry for synthetic biology.

Investigating the dynamics of surface-immobilized DNA nanomachines

Surface-immobilization of molecules can have a profound influence on their structure, function and dynamics. Toehold-mediated strand displacement is often used in solution to drive synthetic nanomachines made from DNA, but the effects of surface-immobilization on the mechanism and kinetics of this reaction have not yet been fully elucidated. Here we show that the kinetics of strand displacement in surface-immobilized nanomachines are significantly different to those of the solution phase reaction, and we attribute this to the effects of intermolecular interactions within the DNA layer. We demonstrate that the dynamics of strand displacement can be manipulated by changing strand length, concentration and G/C content. By inserting mismatched bases it is also possible to tune the rates of the constituent displacement processes (toehold-binding and branch migration) independently, and information can be encoded in the time-dependence of the overall reaction. Our findings will facilitate the rational design of surface-immobilized dynamic DNA nanomachines, including computing devices and track-based motors.

Assessing the potential of surface-immobilized molecular logic machines for integration with solid state technology

Molecular computation with DNA has great potential for low power, highly parallel information processing in a biological or biochemical context. However, significant challenges remain for the field of DNA computation. New technology is needed to allow multiplexed label-free readout and to enable regulation of molecular state without addition of new DNA strands. These capabilities could be provided by hybrid bioelectronic systems in which biomolecular computing is integrated with conventional electronics through immobilization of DNA machines on the surface of electronic circuitry. Here we present a quantitative experimental analysis of a surface-immobilized OR gate made from DNA and driven by strand displacement. The purpose of our work is to examine the performance of a simple representative surface-immobilized DNA logic machine, to provide valuable information for future work on hybrid bioelectronic systems involving DNA devices. We used a quartz crystal microbalance to examine a DNA monolayer containing approximately 5×10(11)gatescm(-2), with an inter-gate separation of approximately 14nm, and we found that the ensemble of gates took approximately 6min to switch. The gates could be switched repeatedly, but the switching efficiency was significantly degraded on the second and subsequent cycles when the binding site for the input was near to the surface. Otherwise, the switching efficiency could be 80% or better, and the power dissipated by the ensemble of gates during switching was approximately 0.1nWcm(-2), which is orders of magnitude less than the power dissipated during switching of an equivalent array of transistors. We propose an architecture for hybrid DNA-electronic systems in which information can be stored and processed, either in series or in parallel, by a combination of molecular machines and conventional electronics. In this architecture, information can flow freely and in both directions between the solution-phase and the underlying electronics via surface-immobilized DNA machines that provide the interface between the molecular and electronic domains.

Assembly of a 3D Cellular Computer Using Folded E-Blocks

The assembly of integrated circuits in three dimensions (3D) provides a possible solution to address the ever-increasing demands of modern day electronic devices. It has been suggested that by using the third dimension, devices with high density, defect tolerance, short interconnects and small overall form factors could be created. However, apart from pseudo 3D architecture, such as monolithic integration, die, or wafer stacking, the creation of paradigms to integrate electronic low-complexity cellular building blocks in architecture that has tile space in all three dimensions has remained elusive. Here, we present software and hardware foundations for a truly 3D cellular computational devices that could be realized in practice. The computing architecture relies on the scalable, self-configurable and defect-tolerant cell matrix. The hardware is based on a scalable and manufacturable approach for 3D assembly using folded polyhedral electronic blocks (E-blocks). We created monomers, dimers and 2 × 2 × 2 assemblies of polyhedral E-blocks and verified the computational capabilities by implementing simple logic functions. We further show that 63.2% more compact 3D circuits can be obtained with our design automation tools compared to a 2D architecture. Our results provide a proof-of-concept for a scalable and manufacture-ready process for constructing massive-scale 3D computational devices.

Configurable NOR gate arrays from Belousov-Zhabotinsky micro-droplets

We investigate the Belousov-Zhabotinsky (BZ) reaction in an attempt to establish a basis for computation using chemical oscillators coupled via inhibition. The system consists of BZ droplets suspended in oil. Interdrop coupling is governed by the non-polar communicator of inhibition, Br2. We consider a linear arrangement of three droplets to be a NOR gate, where the center droplet is the output and the other two are inputs. Oxidation spikes in the inputs, which we define to be TRUE, cause a delay in the next spike of the output, which we read to be FALSE. Conversely, when the inputs do not spike (FALSE) there is no delay in the output (TRUE), thus producing the behavior of a NOR gate. We are able to reliably produce NOR gates with this behavior in microfluidic experiment.

A colorimetric and fluorometric dual-modal DNA logic gate based on the response of a cyanine dye supramolecule to G-quadruplexes

The INHIBIT DNA logic gate with dual-modal outputs based on the response of MTC aggregates to G-quadruplexes.

Surface Enzyme Chemistries for Ultrasensitive Microarray Biosensing with SPR Imaging

The sensitivity and selectivity of surface plasmon resonance imaging (SPRI) biosensing with nucleic acid microarrays can be greatly enhanced by exploiting various nucleic acid ligases, nucleases, and polymerases that manipulate the surface-bound DNA and RNA. We describe here various examples from each of these different classes of surface enzyme chemistries that have been incorporated into novel detection strategies that either drastically enhance the sensitivity of or create uniquely selective methods for the SPRI biosensing of proteins and nucleic acids. A dual-element generator-detector microarray approach that couples a bioaffinity adsorption event on one microarray element to nanoparticle-enhanced SPRI measurements of nucleic acid hybridization adsorption on a different microarray element is used to quantitatively detect DNA, RNA, and proteins at femtomolar concentrations. Additionally, this dual-element format can be combined with the transcription and translation of RNA from surface-bound double-stranded DNA (dsDNA) templates for the on-chip multiplexed biosynthesis of aptamer and protein microarrays in a microfluidic format; these microarrays can be immediately used for real-time SPRI bioaffinity sensing measurements.

What Controls the "Off/On Switch" in the Toehold-Mediated Strand Displacement Reaction on DNA Conjugated Gold Nanoparticles?

In DNA dynamic nanotechnology, a toehold-mediated DNA strand-displacement reaction has demonstrated its capability in building complex autonomous system. In most cases, the reaction is performed in pure DNA solution that is essentially a one-phase system. In the present work, we systematically investigated the reaction in a heterogeneous media, in which the strand that implements a displacing action is conjugated on gold nanoparticles. By monitoring the kinetics of spherical nucleic acid (SNA) assembly driven by toehold-mediated strand displacement reaction, we observed significant differences, i.e., the abrupt jump in behavior of an "off/on switch", in the reaction rate when the invading toehold was extended to eight bases from seven bases. These phenomena are attributed to the effect of steric hindrance arising from the high density of invading strand conjugated to AuNPs. Based on these studies, an INHIBIT logic gate presenting good selectivity was developed.