H-ACO with Consecutive Bases Pairing Constraint for Designing DNA Sequences

DNA computing is a novel computing method that does not rely on traditional computers. The design of DNA sequences is a crucial step in DNA computing, and the quality of the sequence design directly affects the results of DNA computing. In this paper, a new constraint called the consecutive base pairing constraint is proposed to limit specific base pairings in DNA sequence design. Additionally, to improve the efficiency and capability of DNA sequence design, the Hierarchy-ant colony (H-ACO) algorithm is introduced, which combines the features of multiple algorithms and optimizes discrete numerical calculations. Experimental results show that the H-ACO algorithm performs well in DNA sequence design. Finally, this paper compares a series of constraint values and NUPACK simulation data with previous design results, and the DNA sequence set designed in this paper has more advantages.

DNA Logic Gates Integrated on DNA Substrates in Molecular Computing

Due to nucleic acid's programmability, it is possible to realize DNA structures with computing functions, and thus a new generation of molecular computers is evolving to solve biological and medical problems. Pioneered by Milan Stojanovic, Boolean DNA logic gates created the foundation for the development of DNA computers. Similar to electronic computers, the field is evolving towards integrating DNA logic gates and circuits by positioning them on substrates to increase circuit density and minimize gate distance and undesired crosstalk. In this minireview, we summarize recent developments in the integration of DNA logic gates into circuits localized on DNA substrates. This approach of all-DNA integrated circuits (DNA ICs) offers the advantages of biocompatibility, increased circuit response, increased circuit density, reduced unit concentration, facilitated circuit isolation, and facilitated cell uptake. DNA ICs can face similar challenges as their equivalent circuits operating in bulk solution (bulk circuits), and new physical challenges inherent in spatial localization. We discuss possible avenues to overcome these obstacles.

DNA Logic Gates for Small Molecule Activation Circuits in Cells

DNA-based devices such as DNA logic gates self-assemble into supramolecular structures, as dictated by the sequences of the constituent oligonucleotides and their predictable Watson-Crick base pairing interactions. The programmable nature of DNA-based devices permits the design and implementation of DNA circuits that interact in a dynamic and sequential manner capable of spatially arranging disparate DNA species. Here, we report the application of an activatable fluorescence reporter based on a proximity-driven inverse electron demand Diels-Alder (IEDDA) reaction and its robust integration with DNA strand displacement circuits. In response to specific DNA input patterns, sequential strand displacement reactions are initiated and culminate in the hybridization of two modified DNA strands carrying probes capable of undergoing an IEDDA reaction between a vinyl-ether-caged fluorophore and its reactive partner tetrazine, leading to the activation of fluorescence. This approach provides a major advantage for DNA computing in mammalian cells since circuit degradation does not induce fluorescence, in contrast to traditional fluorophore-quencher designs. We demonstrate the robustness and sensitivity of the reporter by testing its ability to serve as a readout for DNA logic circuits of varying complexity inside cells.

Programming and monitoring surface-confined DNA computing

DNA-based molecular computing has evolved to encompass a diverse range of functions, demonstrating substantial promise for both highly parallel computing and various biomedical applications. Recent advances in DNA computing systems based on surface reactions have demonstrated improved levels of specificity and computational speed compared to their solution-based counterparts that depend on three-dimensional molecular collisions. Herein, computational biomolecular interactions confined by various surfaces such as DNA origamis, nanoparticles, lipid membranes and chips are systematically reviewed, along with their manipulation methodologies. Monitoring techniques and applications for these surface-based computing systems are also described. The advantages and challenges of surface-confined DNA computing are discussed.

Implementing Logic Gates by DNA Crystal Engineering

DNA self-assembly computation is attractive for its potential to perform massively parallel information processing at the molecular level while at the same time maintaining its natural biocompatibility. It has been extensively studied at the individual molecule level, but not as much as ensembles in 3D. Here, the feasibility of implementing logic gates, the basic computation operations, in large ensembles: macroscopic, engineered 3D DNA crystals is demonstrated. The building blocks are the recently developed DNA double crossover-like (DXL) motifs. They can associate with each other via sticky-end cohesion. Common logic gates are realized by encoding the inputs within the sticky ends of the motifs. The outputs are demonstrated through the formation of macroscopic crystals that can be easily observed. This study points to a new direction of construction of complex 3D crystal architectures and DNA-based biosensors with easy readouts.

Programmable Molecular Signal Transmission Architecture and Reactant Regeneration Strategy Driven by EXO λ for DNA Circuits

The characteristics of DNA hybridization enable molecular computing through strand displacement reactions, facilitating the construction of complex DNA circuits, which is an important way to realize information interaction and processing at a molecular level. However, signal attenuation in the cascade and shunt process hinders the reliability of the calculation results and further expansion of the DNA circuit scale. Here, we demonstrate a novel programmable exonuclease-assisted signal transmission architecture, where DNA strand with toehold employed to inhibit the hydrolysis process of EXO λ is applied in DNA circuits. We construct a series circuit with variable resistance and a parallel circuit with constant current source, ensuring excellent orthogonal properties between input and output sequences while maintaining low leakage (<5%) during the reaction. Additionally, a simple and flexible exonuclease-driven reactant regeneration (EDRR) strategy is proposed and applied to construct parallel circuits with constant voltage sources that could amplify the output signal without extra DNA fuel strands or energy. Furthermore, we demonstrate the effectiveness of the EDRR strategy in reducing signal attenuation during cascade and shunt processes by constructing a four-node DNA circuit. These findings offer a new approach to enhance the reliability of molecular computing systems and expand the scale of DNA circuits in the future.

Design and Simulation of an Autonomous Molecular Mechanism Using Spatially Localized DNA Computation

As a well-established technique, DNA synthesis offers interesting possibilities for designing multifunctional nanodevices. The micro-processing system of modern semiconductor circuits is dependent on strategies organized on silicon chips to achieve the speedy transmission of substances or information. Similarly, spatially localized structures allow for fixed DNA molecules in close proximity to each other during the synthesis of molecular circuits, thus providing a different strategy that of opening up a remarkable new area of inquiry for researchers. Herein, the Visual DSD (DNA strand displacement) modeling language was used to design and analyze the spatially organized DNA reaction network. The execution rules depend on the hybridization reaction caused by directional complementary nucleotide sequences. A series of DNA strand displacement calculations were organized on the locally coded travel track, and autonomous movement and addressing operations are gradually realized. The DNA nanodevice operates in this manner follows the embedded "molecular program", which improves the reusability and scalability of the same sequence domain in different contexts. Through the communication between various building blocks, the DNA device-carrying the target molecule moves in a controlled manner along the programmed track. In this way, a variety of molecular functional group transport and specific partition storage can be realized. The simulation results of the visual DSD tool provide qualitative and quantitative proof for the operation of the system.

DNA Droplets: Intelligent, Dynamic Fluid

Breathtaking advances in DNA nanotechnology have established DNA as a promising biomaterial for the fabrication of programmable higher-order nano/microstructures. In the context of developing artificial cells and tissues, DNA droplets have emerged as a powerful platform for creating intelligent, dynamic cell-like machinery. DNA droplets are a microscale membrane-free coacervate of DNA formed through phase separation. This new type of DNA system couples dynamic fluid-like property with long-established DNA programmability. This hybrid nature offers an advantageous route to facile and robust control over the structures, functions, and behaviors of DNA droplets. This review begins by describing programmable DNA condensation, commenting on the physical properties and fabrication strategies of DNA hydrogels and droplets. By presenting an overview of the development pathways leading to DNA droplets, it is shown that DNA technology has evolved from static, rigid systems to soft, dynamic systems. Next, the basic characteristics of DNA droplets are described as intelligent, dynamic fluid by showcasing the latest examples highlighting their distinctive features related to sequence-specific interactions and programmable mechanical properties. Finally, this review discusses the potential and challenges of numerical modeling able to connect a robust link between individual sequences and macroscopic mechanical properties of DNA droplets.

DNA-based customized functional modules for signal transformation

Information on the temporal and spatial scale of cellular molecules in biological systems is crucial for estimating life processes and may be conducive to an improved understanding of disease progression. This intracellular and extracellular information is often difficult to obtain at the same time due to the limitations of accessibility and sensing throughput. DNA is an excellent material for in vivo and in vitro applications, and can be used to build functional modules that can transform bio-information (input) into ATCG sequence information (output). Due to their small volume and highly amenable programming, DNA-based functional modules provide an opportunity to monitor a range of information, from transient molecular events to dynamic biological processes. Over the past two decades, with the advent of customized strategies, a series of functional modules based on DNA networks have been designed to gather different information about molecules, including the identity, concentration, order, duration, location, and potential interactions; the action of these modules are based on the principle of kinetics or thermodynamics. This paper summarizes the available DNA-based functional modules that can be used for biomolecular signal sensing and transformation, reviews the available designs and applications of these modules, and assesses current challenges and prospects.

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.

Stochastic approximations of higher-molecular by bi-molecular reactions

Reactions involving three or more reactants, called higher-molecular reactions, play an important role in mathematical modelling in systems and synthetic biology. In particular, such reactions underpin a variety of important bio-dynamical phenomena, such as multi-stability/multi-modality, oscillations, bifurcations, and noise-induced effects. However, as opposed to reactions involving at most two reactants, called bi-molecular reactions, higher-molecular reactions are biochemically improbable. To bridge the gap, in this paper we put forward an algorithm for systematically approximating arbitrary higher-molecular reactions with bi-molecular ones, while preserving the underlying stochastic dynamics. Properties of the algorithm and convergence are established via singular perturbation theory. The algorithm is applied to a variety of higher-molecular biochemical networks, and is shown to play an important role in synthetic biology.

DNA computing for gastric cancer analysis and functional classification

Early identification of key biomarkers of malignant cancer is vital for patients' prognosis and therapies. There is research demonstrating that microRNAs are important biomarkers for cancer analysis. In this article, we used the DNA strand displacement mechanism (DSD) to construct the DNA computing system for cancer analysis. First, gene chips were obtained through bioinformatical training. These microRNA data and clinical traits were obtained from the Cancer Genome Atlas (TCGA) dataset. Second, we analyzed the expression data by using a weighted gene co-expression network (WGCNA) and found four biomarkers for two clinic features, respectively. Last, we constructed a DSD-based DNA computing system for cancer analysis. The inputs of the system are these identified biomarkers; the outputs are the fluorescent signals that represent their corresponding traits. The experiment and simulation results demonstrated the reliability of the DNA computing system. This DSD simulation system is lab-free but clinically meaningful. We expect this innovative method to be useful for rapid and accurate cancer diagnosis.

Massively Parallel DNA Computing Based on Domino DNA Strand Displacement Logic Gates

DNA computing has gained considerable attention due to the characteristics of high-density information storage and high parallel computing for solving computational problems. Building addressable logic gates with biomolecules is the basis for establishing biological computers. In the current calculation model, the multiinput AND operation often needs to be realized through a multilevel cascade between logic gates. Through experiments, it was found that the multilevel cascade causes signal leakage and affects the stability of the system. Using DNA strand displacement technology, we constructed a domino-like multiinput AND gate computing system instead of a cascade of operations, realizing multiinput AND computing on one logic gate and abandoning the traditional multilevel cascade of operations. Fluorescence experiments demonstrated that our methods significantly reduce system construction costs and improve the stability and robustness of the system. Finally, we proved stability and robustness of the domino AND gate by simulating the tic-tac-toe process with a massively parallel computing strategy.

Design and Realization of Triple dsDNA Nanocomputing Circuits in Microfluidic Chips

DNA logic gates, nanocomputing circuits, have already implemented basic computations and shown great signal potential for nano logic material application. However, the reaction temperature and computing speed still limit its development. Performing complicated computations requires a more stable component and a better computing platform. We proposed a more stable design of logic gates based on a triple, double-stranded, DNA (T-dsDNA) structure. We demonstrated a half adder and a full adder using these DNA nanocircuits and performed the computations in a microfluidic chip device at room temperature. When the solutions were mixed in the device, we obtained the expected results in real time, which suggested that the T-dsDNA combined microfluidic chip provides a concise strategy for large DNA nanocircuits.

DNA Matrix Operation Based on the Mechanism of the DNAzyme Binding to Auxiliary Strands to Cleave the Substrate

Numerical computation is a focus of DNA computing, and matrix operations are among the most basic and frequently used operations in numerical computation. As an important computing tool, matrix operations are often used to deal with intensive computing tasks. During calculation, the speed and accuracy of matrix operations directly affect the performance of the entire computing system. Therefore, it is important to find a way to perform matrix calculations that can ensure the speed of calculations and improve the accuracy. This paper proposes a DNA matrix operation method based on the mechanism of the DNAzyme binding to auxiliary strands to cleave the substrate. In this mechanism, the DNAzyme binding substrate requires the connection of two auxiliary strands. Without any of the two auxiliary strands, the DNAzyme does not cleave the substrate. Based on this mechanism, the multiplication operation of two matrices is realized; the two types of auxiliary strands are used as elements of the two matrices, to participate in the operation, and then are combined with the DNAzyme to cut the substrate and output the result of the matrix operation. This research provides a new method of matrix operations and provides ideas for more complex computing systems.

CRISPR-Powered DNA Computing and Digital Display

Clustered regularly interspaced short palindromic repeats (CRISPR) technology has unique specificity for recognizing and cleaving target DNA complementary to the CRISPR guide sequence. Here, we report on a CRISPR-powered DNA computing and digital display system in which programmed DNA targets serve as the input and an ON/OFF fluorescence signal indicates a TRUE/FALSE output. This system allows the establishment of a one-to-one relationship between input and output, enabling multilevel DNA logic computing. Applying pre-CRISPR reactions that selectively maintain or inhibit CRISPR reactivity can further improve the computing capability by expanding input size. In particular, we present a paper-based microfluidic chip with freeze-dried CRISPR reaction mixtures that are programmed to digitally display the results of functional operations, including square, cube, and square-root operations. This strategy allows information decoding and displaying as well, which brings potential in next-generation DNA steganography and cryptography. We envision that the intrinsic orthogonality of CRISPR provides a new paradigm for DNA computing and molecular programming.

Development of Synthetic DNA Circuit and Networks for Molecular Information Processing

Deoxyribonucleic acid (DNA), a genetic material, encodes all living information and living characteristics, e.g., in cell, DNA signaling circuits control the transcription activities of specific genes. In recent years, various DNA circuits have been developed to implement a wide range of signaling and for regulating gene network functions. In particular, a synthetic DNA circuit, with a programmable design and easy construction, has become a crucial method through which to simulate and regulate DNA signaling networks. Importantly, the construction of a hierarchical DNA circuit provides a useful tool for regulating gene networks and for processing molecular information. Moreover, via their robust and modular properties, DNA circuits can amplify weak signals and establish programmable cascade systems, which are particularly suitable for the applications of biosensing and detecting. Furthermore, a biological enzyme can also be used to provide diverse circuit regulation elements. Currently, studies regarding the mechanisms and applications of synthetic DNA circuit are important for the establishment of more advanced artificial gene regulation systems and intelligent molecular sensing tools. We therefore summarize recent relevant research progress, contributing to the development of nanotechnology-based synthetic DNA circuits. By summarizing the current highlights and the development of synthetic DNA circuits, this paper provides additional insights for future DNA circuit development and provides a foundation for the construction of more advanced DNA circuits.

Solving 0-1 Integer Programming Problem Based on DNA Strand Displacement Reaction Network

Chemical reaction networks (CRNs) based on DNA strand displacement (DSD) can be used as an effective programming language for solving various mathematical problems. In this paper, we design three chemical reaction modules by using the DNA strand displacement reaction as the basic principle, with a weighted reaction module, sum reaction module, and threshold reaction module. These modules are used as basic elements to form chemical reaction networks that can be used to solve 0-1 integer programming problems. The problem can be solved through the three steps of weighting, sum, and threshold, and then the results of the operations can be expressed through a single-stranded DNA output with fluorescent molecules. Finally, we use biochemical experiments and Visual DSD simulation software to verify and evaluate the chemical reaction networks. The results have shown that the DSD-based chemical reaction networks constructed in this paper have good feasibility and stability.

Multi-Mode Reconfigurable DNA-Based Chemical Reaction Circuits for Soft Matter Computing and Control

Developing smart material systems for performing different tasks in diverse environments remains challenging. Here, we show that by integrating stimuli-responsive soft materials with multi-mode reconfigurable DNA-based chemical reaction circuits (D-CRCs), it can control size change of microgels with multiple reaction pathways and adapt expansion behaviors to meet diverse environments. We first use pH-responsive intramolecular conformational switches for regulating DNA strand displacement reactions (SDRs). The ability to regulate SDRs with tunable pH-dependence allows to build dynamic chemical reaction networks with diverse reaction pathways. We confirm that the designed DNA switching circuits are reconfigurable at different pH and perform different logic operations, and the swelling of DNA switching circuit-integrated microgel systems can be programmably directed by D-CRCs. Our approach provides insight into building smart responsive materials and fabricating autonomous soft robots.

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.

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.

Biocomputing Based on DNA Strand Displacement Reactions

The high sequence specificity and precise base complementary pairing principle of DNA provides a rich orthogonal molecular library for molecular programming, making it one of the most promising materials for developing bio-compatible intelligence. In recent years, DNA has been extensively studied and applied in the field of biological computing. Among them, the toehold-mediated strand displacement reaction (SDR) with properties including enzyme free, flexible design and precise control, have been extensively used to construct biological computing circuits. This review provides a systemic overview of SDR design principles and the applications. Strategies for designing DNA-only, enzymes-assisted, other molecules-involved and external stimuli-controlled SDRs are described. The recently realized computing functions and the application of DNA computing in other fields are introduced. Finally, the advantages and challenges of SDR-based computing are discussed.

Stable DNA Sequence Over Close-Ending and Pairing Sequences Constraint

DNA computing is a new method based on molecular biotechnology to solve complex problems. The design of DNA sequences is a multi-objective optimization problem in DNA computing, whose objective is to obtain optimized sequences that satisfy multiple constraints to improve the quality of the sequences. However, the previous optimized DNA sequences reacted with each other, which reduced the number of DNA sequences that could be used for molecular hybridization in the solution and thus reduced the accuracy of DNA computing. In addition, a DNA sequence and its complement follow the principle of complementary pairing, and the sequence of base GC at both ends is more stable. To optimize the above problems, the constraints of Pairing Sequences Constraint (PSC) and Close-ending along with the Improved Chaos Whale (ICW) optimization algorithm were proposed to construct a DNA sequence set that satisfies the combination of constraints. The ICW optimization algorithm is added to a new predator–prey strategy and sine and cosine functions under the action of chaos. Compared with other algorithms, among the 23 benchmark functions, the new algorithm obtained the minimum value for one-third of the functions and two-thirds of the current minimum value. The DNA sequences satisfying the constraint combination obtained the minimum of fitness values and had stable and usable structures.

[The current status and future prospects of DNA computing]

As the demand for high-performance computing continues to grow, traditional computing models are facing unprecedented challenges. Among the many emerging computing technologies, DNA computing has attracted much attention due to its low energy consumption and parallelism. The DNA circuit, which is the basis for DNA computing, is an important technology for the regulation and processing of the molecular information. This review highlights the basic principles of DNA computing, summarizes the latest research progress, and concludes with a discussion of the challenges of DNA computing. Such integrated molecular computing systems are expected to be widely used in the fields of aerospace, information security and defense system.

Quasi-robust control of biochemical reaction networks via stochastic morphing

One of the main objectives of synthetic biology is the development of molecular controllers that can manipulate the dynamics of a given biochemical network that is at most partially known. When integrated into smaller compartments, such as living or synthetic cells, controllers have to be calibrated to factor in the intrinsic noise. In this context, biochemical controllers put forward in the literature have focused on manipulating the mean (first moment) and reducing the variance (second moment) of the target molecular species. However, many critical biochemical processes are realized via higher-order moments, particularly the number and configuration of the probability distribution modes (maxima). To bridge the gap, we put forward the stochastic morpher controller that can, under suitable timescale separations, morph the probability distribution of the target molecular species into a predefined form. The morphing can be performed at a lower-resolution, allowing one to achieve desired multi-modality/multi-stability, and at a higher-resolution, allowing one to achieve arbitrary probability distributions. Properties of the controller, such as robustness and convergence, are rigorously established, and demonstrated on various examples. Also proposed is a blueprint for an experimental implementation of stochastic morpher.

Novel Modalities in DNA Data Storage

The field of storing information in DNA has expanded exponentially. Most common modalities involve encoding information from bits into synthesized nucleotides, storage in liquid or dry media, and decoding via sequencing. However, limitations to this paradigm include the cost of DNA synthesis and sequencing, along with low throughput. Further unresolved questions include the appropriate media of storage and the scalability of such approaches for commercial viability. In this review, we examine various storage modalities involving the use of DNA from a systems-level perspective. We compare novel methods that draw inspiration from molecular biology techniques that have been devised to overcome the difficulties posed by standard workflows and conceptualize potential applications that can arise from these advances.

DNAr: An R Package to Simulate and Analyze CRN and DSD Networks

Chemical reaction networks (CRNs) have been proposed as an abstraction for molecular computing. DNA strand displacement (DSD) reactions are good candidates to realize this endeavor, since DNA strands can be wired to implement the desired dynamic behavior in a test tube. Specialists use simulators to help them design such chemical systems before experimental implementation. In this sense, we present the DNAr package, an alternative open-source tool, developed in R language, for users from multidisciplinary areas. The current version of our tool offers functions to simulate CRNs, convert a formal CRN into a DSD network, interpret results, export to Visual DSD, and create libraries. Here, we use the consensus CRN to show DNAr features and a neural network model to demonstrate scalability, simulating more than 600 chemical reactions in a few minutes.

Decoding DNA data storage for investment

While DNA's perpetual role in biology and life science is well documented, its burgeoning digital applications are beginning to garner significant interest. As the development of novel technologies requires continuous research, product development, startup creation, and financing, this work provides an overview of each respective area and highlights current trends, challenges, and opportunities. These are supported by numerous interviews with key opinion leaders from across academia, government agencies and the commercial sector, as well as investment data analysis. Our findings illustrate the societal and economic need for technological innovation and disruption in data storage, paving the way for nature's own time-tested, advantageous, and unrivaled solution. We anticipate a significant increase in available investment capital and continuous scientific progress, creating a ripe environment on which DNA data storage-enabling startups can capitalize to bring DNA data storage into daily life.

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Overview on current DNA data storage technologies and commercialization hurdles

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Insights from leading DNA data storage experts and investment financing data

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DNA synthesis remains the biggest challenge in the industry

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Archiving cold data is the low-hanging fruit in DNA data storage

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Upwards trend in investment landscape suggests optimal startup fundraising period

Parallelizing Assignment Problem with DNA Strands

Background

Many problems of combinatorial optimization, which are solvable only in exponential time, are known to be Non-Deterministic Polynomial hard (NP-hard). With the advent of parallel machines, new opportunities have been emerged to develop the effective solutions for NP-hard problems. However, solving these problems in polynomial time needs massive parallel machines and is not applicable up to now.

Objectives

DNA (Deoxyribonucleic acid) computing provides a fantastic method to solve NP-hard problems in polynomial time. Accordingly, one of the famous NP-hard problems is assignment problem, which is designed to find the best assignment of n jobs to n persons in a way that it could maximize the profit or minimize the cost.

Material and Methods

Applying bio molecular operations of Adelman Lipton model, a novel parallel DNA algorithm have been proposed for solving the assignment problem.

Results

The proposed algorithm can solve the problem in time complexity, and just O(n²) initial DNA strand in comparison with nn initial sequence, which is used by the other methods.

Conclusions

In this article, using DNA computing, we proposed a parallel DNA algorithm to solve the assignment problem in linear time.

Recent Advances in Liposome-Based Molecular Robots

A molecular robot is a microorganism-imitating micro robot that is designed from the molecular level and constructed by bottom-up approaches. As with conventional robots, molecular robots consist of three essential robotics elements: control of intelligent systems, sensors, and actuators, all integrated into a single micro compartment. Due to recent developments in microfluidic technologies, DNA nanotechnologies, synthetic biology, and molecular engineering, these individual parts have been developed, with the final picture beginning to come together. In this review, we describe recent developments of these sensors, actuators, and intelligence systems that can be applied to liposome-based molecular robots. First, we explain liposome generation for the compartments of molecular robots. Next, we discuss the emergence of robotics functions by using and functionalizing liposomal membranes. Then, we discuss actuators and intelligence via the encapsulation of chemicals into liposomes. Finally, the future vision and the challenges of molecular robots are described.

Simple Repeats as Building Blocks for Genetic Computers

Processing of RNA involves heterogeneous nuclear ribonucleoproteins. The simple sequence repeats (SSRs) they bind can also adopt alternative DNA structures, like Z DNA, triplexes, G quadruplexes, and I motifs. Those SSRs capable of switching conformation under physiological conditions (called flipons) are genetic elements that can encode alternative RNA processing by their effects on RNA processivity, most likely as DNA:RNA hybrids. Flipons are elements of a binary, instructive genetic code directing how genomic sequences are compiled into transcripts. The combinatorial nature of this code provides a rich set of options for creating genetic computers able to reproduce themselves and use a heritable and evolvable code to optimize survival. The underlying computational logic potentiates a diverse set of genetic programs that modify cis-mediated heritability and disease risk.

Demonstration of Arithmetic Calculations by DNA Tile-Based Algorithmic Self-Assembly

Owing to its high information density, energy efficiency, and massive parallelism, DNA computing has undergone several advances and made significant contributions to nanotechnology. Notably, arithmetic calculations implemented by multiple logic gates such as adders and subtractors have received much attention because of their well-established logic algorithms and feasibility of experimental implementation. Although small molecules have been used to implement these computations, a DNA tile-based calculator has been rarely addressed owing to complexity of rule design and experimental challenges for direct verification. Here, we construct a DNA-based calculator with three types of building blocks (propagator, connector, and solution tiles) to perform addition and subtraction operations through algorithmic self-assembly. An atomic force microscope is used to verify the solutions. Our method provides a potential platform for the construction of various types of DNA algorithmic crystals (such as flip-flops, encoders, and multiplexers) by embedding multiple logic gate operations in the DNA base sequences.

Digital Sensing and Molecular Computation by an Enzyme-Free DNA Circuit

DNA circuits form the basis of programmable molecular systems capable of signal transduction and algorithmic computation. Some classes of molecular programs, such as catalyzed hairpin assembly, enable isothermal, enzyme-free signal amplification. However, current detection limits in DNA amplification circuits are modest, as sensitivity is inhibited by signal leakage resulting from noncatalyzed background reactions inherent to the noncovalent system. Here, we overcome this challenge by optimizing a catalyzed hairpin assembly for single-molecule sensing in a digital droplet assay. Furthermore, we demonstrate digital reporting of DNA computation at the single-molecule level by employing ddCHA as a signal transducer for simple DNA logic gates. By facilitating signal transduction of molecular computation at pM concentration, our approach can improve processing density by a factor of 10⁴ relative to conventional DNA logic gates. More broadly, we believe that digital molecular computing will broaden the scope and efficacy of isothermal amplification circuits within DNA computing, biosensing, and signal amplification in general.

In Vitro Identification of the Hamiltonian Cycle Using a Circular Structure Assisted DNA Computer

Adleman's illustration of molecular computing using DNA paved the way toward an entirely new direction of computing (Adleman, L. M. Science 1994, 266, 1021). The exponential time complex combinatorial problem on a traditional computer turns out to be a separation problem involving a polynomial number of steps in DNA computing experiments. Despite being a promising concept, the implementations of existing DNA computing procedures were restricted only to the smaller size formulations. In this work, we demonstrate a structure assisted DNA computing procedure on a bigger size Hamiltonian cycle problem involving 18 vertices. The developed model involves the formation and digestion of circular structure DNA, iteratively over multiple stages to eliminate the incorrect solutions to the given combinatorial problem. A high accuracy is obtained compared to other structure assisted models, which enable one to solve the bigger size problems.

Ten-Input Cube Root Logic Computation with Rational Designed DNA Nanoswitches Coupled with DNA Strand Displacement Process

The predictability of Watson-Crick base-pairing provides a unique structural programmability to DNAs, promoting a facile design of bimolecular reactions that perform computation. However, most of the current architectures could only implement limited logical circuits and are incapable of handling more complex mathematical operations, thus limiting computing devices from advancing to the next-stage functional complexity. Here, by designing a multifunctional DNA-based reaction platform coupled with multiple fluorescent substrates as output reporters, we construct, for the first time, a logic circuit that can compute the cube root of a 10-bit binary number (within the decimal number 1000). This relatively large-scale logic system with 10 inputs and four outputs showcases the power of DNAs in the field of biological computing and will potentially open up a new horizon for designing novel functional devices and complex computing circuits and bringing breakthroughs in biocomputing.

A Novel Image Encryption Approach Based on a Hyperchaotic System, Pixel-Level Filtering with Variable Kernels, and DNA-Level Diffusion

With the rapid growth of image transmission and storage, image security has become a hot topic in the community of information security. Image encryption is a direct way to ensure image security. This paper presents a novel approach that uses a hyperchaotic system, Pixel-level Filtering with kernels of variable shapes and parameters, and DNA-level Diffusion, so-called PFDD, for image encryption. The PFDD totally consists of four stages. First, a hyperchaotic system is applied to generating hyperchaotic sequences for the purpose of subsequent operations. Second, dynamic filtering is performed on pixels to change the pixel values. To increase the diversity of filtering, kernels with variable shapes and parameters determined by the hyperchaotic sequences are used. Third, a global bit-level scrambling is conducted to change the values and positions of pixels simultaneously. The bit stream is then encoded into DNA-level data. Finally, a novel DNA-level diffusion scheme is proposed to further change the image values. We tested the proposed PFDD with 15 publicly accessible images with different sizes, and the results demonstrate that the PFDD is capable of achieving state-of-the-art results in terms of the evaluation criteria, indicating that the PFDD is very effective for image encryption.

Improving the Quantification of DNA Sequences Using Evolutionary Information Based on Deep Learning

It is known that over 98% of the human genome is non-coding, and 93% of disease associated variants are located in these regions. Therefore, understanding the function of these regions is important. However, this task is challenging as most of these regions are not well understood in terms of their functions. In this paper, we introduce a novel computational model based on deep neural networks, called DQDNN, for quantifying the function of non-coding DNA regions. This model combines convolution layers for capturing regularity motifs at multiple scales and recurrent layers for capturing long term dependencies between the captured motifs. In addition, we show that integrating evolutionary information with raw genomic sequences improves the performance of the predictor significantly. The proposed model outperforms the state-of-the-art ones using raw genomics sequences only and also by integrating evolutionary information with raw genomics sequences. More specifically, the proposed model improves 96.9% and 98% of the targets in terms of area under the receiver operating characteristic curve and the precision-recall curve, respectively. In addition, the proposed model improved the prioritization of functional variants of expression quantitative trait loci (eQTLs) compared with the state-of-the-art models.

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 ) .

Repeated Reuse of Deoxyribozyme-Based Logic Gates

Deoxyribozymes (DNAzymes) have demonstrated a significant capacity for biocomputing and hold promise for information processing within advanced biological devices if several key capabilities are developed. One required capability is reuse-having DNAzyme logic gates be cyclically, and controllably, activated and deactivated. We designed an oligonucleotide-based system for DNAzyme reuse that could (1) remove previously bound inputs by addition of complementary oligonucleotides via toe-hold mediated binding and (2) diminish output signal through the addition of quencher-labeled oligonucleotides complementary to the fluorophore-bound substrate. Our system demonstrated, for the first time, the ability for DNAzymes to have their activity toggled, with activity returning to 90-125% of original activity. This toggling could be performed multiple times with control being exerted over when the toggling occurs, with three clear cycles observed before the variability in activity became too great. Our data also demonstrated that fluorescent output of the DNAzyme activity could be actively removed and regenerated. This reuse system can increase the efficiency of DNAzyme-based logic circuits by reducing the number of redundant oligonucleotides and is critical for future development of reusable biodevices controlled by logical operations.

DNA-strand molecular beacon optical processor

Due to the characteristics of the newly developed DNA computing, many researchers are interested in this specialty. One advantage of DNA " Deoxyribonucleic acid" is that it has ability to resolve a Boolean circuit with various types of gates at the same time in a single level. Most of the prior models suffered from the limitations that each level of the circuit requests the gates to be of some kind. The model proposed in this work increases parallelism and reduces human intervention to a tremendous extent. When level-wise simulation is executed, the simulation for each model shows the decrease in the number of nitrogen bases used, which leads to the processing of the largest number of data with the ability to increase the length of a word, in addition to the adoption of the parallel principle of implementation. The model is designed on a mechanism which includes adder and multiplier.

A computational framework for DNA sequencing microscopy

We describe a method whereby microscale spatial information such as the relative positions of biomolecules on a surface can be transferred to a sequence-based format and reconstructed into images without conventional optics. Barcoded DNA "polymerase colony" (polony) amplification techniques enable one to distinguish specific locations of a surface by their sequence. Image formation is based on pairwise fusion of uniquely tagged and spatially adjacent polonies. The network of polonies connected by shared borders forms a graph whose topology can be reconstructed from pairs of barcodes fused during a polony cross-linking phase, the sequences of which are determined by recovery from the surface and next-generation (next-gen) sequencing. We developed a mathematical and computational framework for this principle called polony adjacency reconstruction for spatial inference and topology and show that Euclidean spatial data may be stored and transmitted in the form of graph topology. Images are formed by transferring molecular information from a surface of interest, which we demonstrated in silico by reconstructing images formed from stochastic transfer of hypothetical molecular markers. The theory developed here could serve as a basis for an automated, multiplexable, and potentially superresolution imaging method based purely on molecular information.

A novel bio-heuristic computing algorithm to solve the capacitated vehicle routing problem based on Adleman-Lipton model

DNA computing, as one of potential means to solve complicated computational problems, is a new field of interdisciplinary research, including computational mathematics, parallel algorithms, bioinformatics. Capacitated vehicle routing problem is one of famous NP-hard problems, which includes determining the path of a group same vehicles serving a set of clients, while minimizing the total transportation cost. Based on the bio-heuristic computing model and DNA molecular manipulations, parallel biocomputing algorithms for solving capacitated vehicle routing problem are proposed in this paper. We appropriately use different biological chains to mean vertices, edges, weights, and adopt appropriate biological operations to search the solutions of the problem with O(n²) time complexity. We enrich the application scope of biocomputing, reduce computational complexity, and verify practicability of DNA parallel algorithms through simulations.

A Logic Programming Language for Computational Nucleic Acid Devices

Computational nucleic acid devices show great potential for enabling a broad range of biotechnology applications, including smart probes for molecular biology research, in vitro assembly of complex compounds, high-precision in vitro disease diagnosis and, ultimately, computational theranostics inside living cells. This diversity of applications is supported by a range of implementation strategies, including nucleic acid strand displacement, localization to substrates, and the use of enzymes with polymerase, nickase, and exonuclease functionality. However, existing computational design tools are unable to account for these strategies in a unified manner. This paper presents a logic programming language that allows a broad range of computational nucleic acid systems to be designed and analyzed. The language extends standard logic programming with a novel equational theory to express nucleic acid molecular motifs. It automatically identifies matching motifs present in the full system, in order to apply a specified transformation expressed as a logical rule. The language supports the definition of logic predicates, which provide constraints that need to be satisfied in order for a given rule to be applied. The language is sufficiently expressive to encode the semantics of nucleic strand displacement systems with complex topologies, together with computation performed by a broad range of enzymes, and is readily extensible to new implementation strategies. Our approach lays the foundation for a unifying framework for the design of computational nucleic acid devices.

Biocomputing with Nanostructures on Lipid Bilayers

Biocomputation is the algorithmic manipulation of biomolecules. Nanostructures, most notably DNA nanostructures and nanoparticles, become active substrates for biocomputation when modified with stimuli-responsive, programmable biomolecular ligands. This approach-biocomputing with nanostructures ("nano-bio computing")-allows autonomous control of matter and information at the nanoscale; their dynamic assemblies and beneficial properties can be directed without human intervention. Recently, lipid bilayers interfaced with nanostructures have emerged as a new biocomputing platform. This new nano-bio interface, which exploits lipid bilayers as a chemical circuit board for information processing, offers a unique reaction space for realizing nanostructure-based computation at a previously unexplored dimension. In this Concept, recent advances in nano-bio computing are briefly reviewed and the newly emerging concept of biocomputing with nanostructures on lipid bilayers is introduced.

Dynamically NAND gate system on DNA origami template

Molecular logic gates play an important role in many fields and DNA-based logic gates are the basis of DNA computers. A dynamically NAND gate system on the DNA origami template is established in this paper. Naturally, the system is stable in solution without any reaction. Different logical values are mapped into different DNA input strands. When logical values are entered into the system, the corresponding DNA input strands undergo a directed hybridization chain reaction (HCR) at corresponding positions on the DNA origami template. The operation results are identified by disassembly between the nanogold particles (AuNPs) and DNA origami template. The nanogold particles remain on the DNA origami template, indicating that the result is true; The nanogold particles are dynamically separated from the DNA origami template, indicating that the result is false. The simulation of the system through Visual DSD shows that the reaction strictly followed the designed direction, and no error products are generated during the reaction. These simulation results show that the system has the advantages of feasibility, stability and intelligence.

Enzymatic Weight Update Algorithm for DNA-Based Molecular Learning

Recent research in DNA nanotechnology has demonstrated that biological substrates can be used for computing at a molecular level. However, in vitro demonstrations of DNA computations use preprogrammed, rule-based methods which lack the adaptability that may be essential in developing molecular systems that function in dynamic environments. Here, we introduce an in vitro molecular algorithm that ‘learns’ molecular models from training data, opening the possibility of ‘machine learning’ in wet molecular systems. Our algorithm enables enzymatic weight update by targeting internal loop structures in DNA and ensemble learning, based on the hypernetwork model. This novel approach allows massively parallel processing of DNA with enzymes for specific structural selection for learning in an iterative manner. We also introduce an intuitive method of DNA data construction to dramatically reduce the number of unique DNA sequences needed to cover the large search space of feature sets. By combining molecular computing and machine learning the proposed algorithm makes a step closer to developing molecular computing technologies for future access to more intelligent molecular systems.

Microcompartments for Protection and Isolation of Nanoscale DNA Computing Elements

Physical isolation of molecular computing elements holds the potential for increasing system complexity by enabling the reuse of standardized components and by protecting the components from environmental degradation. However, once elements have been compartmentalized, methods for communicating into these compartments are needed. We report the compartmentalization of steroid-responsive DNA aptamers within giant unilamellar vesicles (GUVs) that are permeable to steroid inputs. Monodisperse GUVs are loaded with aptamers using a microfluidic platform. We demonstrate the target-specific activation of individual aptamers within the GUVs and then load two noninterfering aptamers into the same GUV and demonstrate specific responses to all possible combinations of the two input steroids. Crucially, GUVs prevent the degradation of DNA components by nucleases, providing a potential mechanism for deploying nucleic acid components in vivo. Importantly, our compartments also prevent nonspecific cross-talk between complementary strands, thereby providing a method for parallel execution of cross-reacting molecular logic components. Thus, we provide a mechanism for spatially organizing molecular computing elements, which will increase system modularity by allowing standardized components to be reused.

Image Encryption Based on Pixel-Level Diffusion with Dynamic Filtering and DNA-Level Permutation with 3D Latin Cubes

Image encryption is one of the essential tasks in image security. In this paper, we propose a novel approach that integrates a hyperchaotic system, pixel-level Dynamic Filtering, DNA computing, and operations on 3D Latin Cubes, namely DFDLC, for image encryption. Specifically, the approach consists of five stages: (1) a newly proposed 5D hyperchaotic system with two positive Lyapunov exponents is applied to generate a pseudorandom sequence; (2) for each pixel in an image, a filtering operation with different templates called dynamic filtering is conducted to diffuse the image; (3) DNA encoding is applied to the diffused image and then the DNA-level image is transformed into several 3D DNA-level cubes; (4) Latin cube is operated on each DNA-level cube; and (5) all the DNA cubes are integrated and decoded to a 2D cipher image. Extensive experiments are conducted on public testing images, and the results show that the proposed DFDLC can achieve state-of-the-art results in terms of several evaluation criteria.

Recent Progress in Fluorescence Signal Design for DNA-Based Logic Circuits

DNA-based logic circuits, encoding algorithms in DNA and processing information, are pushing the frontiers of molecular computers forward, owing to DNA's advantages of stability, accessibility, manipulability, and especially inherent biological significance and potential medical application. In recent years, numerous logic functions, from arithmetic to nonarithmetic, have been realized based on DNA. However, DNA can barely provide a detectable signal by itself, so that the DNA-based circuits depend on extrinsic signal actuators. The signal strategy of carrying out a response is becoming one of the design focuses in DNA-based logic circuit construction. Although work on sequence and structure design for DNA-based circuits has been well reviewed, the strategy on signal production lacks comprehensive summary. In this review, we focused on the latest designs of fluorescent output for DNA-based logic circuits. Several basic strategies are summarized and a few designs for developing multi-output systems are provided. Finally, some current difficulties and possible opportunities were also discussed.

Renewable Time-Responsive DNA Circuits

DNA devices have been shown to be capable of evaluating Boolean logic. Several robust designs for DNA circuits have been demonstrated. Some prior DNA-based circuits are use-once circuits since the gate motifs of the DNA circuits get permanently destroyed as a side effect of the computation, and hence cannot respond correctly to subsequent changes in inputs. Other DNA-based circuits use a large reservoir of buffered gates to replace the working gates of the circuit and can be used to drive a finite number of computation cycles. In many applications of DNA circuits, the inputs are inherently asynchronous, and this necessitates that the DNA circuits be asynchronous: the output must always be correct regardless of differences in the arrival time of inputs. This paper demonstrates: 1) renewable DNA circuits, which can be manually reverted to their original state by addition of DNA strands, and 2) time-responsive DNA circuits, where if the inputs change over time, the DNA circuit can recompute the output correctly based on the new inputs, that are manually added after the system has been reset. The properties of renewable, asynchronous, and time-responsiveness appear to be central to molecular-scale systems; for example, self-regulation in cellular organisms.