Probabilistic switching circuits in DNA

Arbitrary Digital DNA Computing: A Programmable Molecular Perceptron Driven by Lambda Exonuclease for Lighting up Concatenated Circuits

DNA circuits, as a type of biochemical system, have the capability to synchronize the perception of molecular information with a chemical reaction response and directly process the molecular characteristic information in biological activities, making them a crucial area in molecular digital computing and smart bioanalytical applications. Instead of cascading logic gates, the traditional research approach achieves multiple logic operations which limits the scalability of DNA circuits and increases the development costs. Based on the interface reaction mechanism of Lambda exonuclease, the molecular perceptron proposed in this study, with the need for only adjusting weight and bias parameters to alter the corresponding logic expressions, enhances the versatility of the molecular circuits. We also establish a mathematical model and an improved heuristic algorithm for solving weights and bias parameters for arbitrary logic operations. The simulation and FRET experiment results of a series of logic operations demonstrate the universality of molecular perceptron. We hope the proposed molecular perceptron can introduce a new design paradigm for molecular circuits, fostering innovation and development in biomedical research related to biosensing, targeted therapy, and nanomachines.

DNA as a universal chemical substrate for computing and data storage

DNA computing and DNA data storage are emerging fields that are unlocking new possibilities in information technology and diagnostics. These approaches use DNA molecules as a computing substrate or a storage medium, offering nanoscale compactness and operation in unconventional media (including aqueous solutions, water-in-oil microemulsions and self-assembled membranized compartments) for applications beyond traditional silicon-based computing systems. To build a functional DNA computer that can process and store molecular information necessitates the continued development of strategies for computing and data storage, as well as bridging the gap between these fields. In this Review, we explore how DNA can be leveraged in the context of DNA computing with a focus on neural networks and compartmentalized DNA circuits. We also discuss emerging approaches to the storage of data in DNA and associated topics such as the writing, reading, retrieval and post-synthesis editing of DNA-encoded data. Finally, we provide insights into how DNA computing can be integrated with DNA data storage and explore the use of DNA for near-memory computing for future information technology and health analysis applications.

pH-Controlled Resettable Modular DNA Strand-Displacement Circuits

Sophisticated dynamic molecular systems with diverse functions have been fabricated by using the fundamental tool of toehold-mediated strand displacement (TMSD) in the field of dynamic DNA nanotechnology. However, simple approaches to reset these TMSD-based dynamic systems are lacking due to the difficulty in creating kinetically favored pathways to implement the backward resetting reactions. Here, we develop a facile proton-driven strategy to achieve complete resetting of a modular DNA circuit by integrating a pH-responsive intermolecular CG-C+ triplex DNA and an i-motif DNA into the conventional DNA substrate. The pH-programmed strategy allows modular DNA components to specifically associate/dissociate to promote the forward/backward TMSD reactions, thereby enabling the modular DNA circuit to be repeatedly operated at a constant temperature without generating any DNA waste products. Leveraging this tractable approach, we further constructed two resettable DNA logic gates used for logical computation and two resettable catalytic DNA systems with good performance in signal transduction and amplification.

A nicking enzyme-assisted allosteric strategy for self-resetting DNA switching circuits

The self-regulation of biochemical reaction networks is crucial for maintaining balance, stability, and adaptability within biological systems. DNA switching circuits, serving as basic units, play essential roles in regulating pathways, facilitating signal transduction, and processing biochemical reaction networks. However, the non-reusability of DNA switching circuits hinders its application in current complex information processing. Herein, we proposed a nicking enzyme-assisted allosteric strategy for constructing self-resetting DNA switching circuits to realize complex information processing. This strategy utilizes the unique cleavage ability of the nicking enzyme to achieve the automatic restoration of states. Based on this strategy, we implemented a self-resetting DNA switch. By leveraging the reusability of the DNA switch, we constructed a DNA switching circuit with selective activation characteristics and further extended its functionality to include fan-out and fan-in processes by expanding the number of functional modules and connection modes. Furthermore, we demonstrated the complex information processing capabilities of these switching circuits by integrating recognition, translation, and decision functional modules, which could analyze and transmit multiple input signals and realize parallel logic operations. This strategy simplifies the design of switching circuits and promotes the future development of biosensing, molecular computing, and nanomachines.

Neural network execution using nicked DNA and microfluidics

DNA has been discussed as a potential medium for data storage. Potentially it could be denser, could consume less energy, and could be more durable than conventional storage media such as hard drives, solid-state storage, and optical media. However, performing computations on the data stored in DNA is a largely unexplored challenge. This paper proposes an integrated circuit (IC) based on microfluidics that can perform complex operations such as artificial neural network (ANN) computation on data stored in DNA. We envision such a system to be suitable for highly dense, throughput-demanding bio-compatible applications such as an intelligent Organ-on-Chip or other biomedical applications that may not be latency-critical. It computes entirely in the molecular domain without converting data to electrical form, making it a form of in-memory computing on DNA. The computation is achieved by topologically modifying DNA strands through the use of enzymes called nickases. A novel scheme is proposed for representing data stochastically through the concentration of the DNA molecules that are nicked at specific sites. The paper provides details of the biochemical design, as well as the design, layout, and operation of the microfluidics device. Benchmarks are reported on the performance of neural network computation.

Construction of DNA-based molecular circuits using normally open and normally closed switches driven by lambda exonuclease

Building synthetic molecular circuits is an important way to realize ion detection, information processing, and molecular computing. However, it is still challenging to implement the NOT logic controlled by a single molecule input in synthetic molecular circuits wherein the presence or absence of the molecule represents the ON or OFF state of the input. Here, based on lambda exonuclease (λ exo), for the first time, we propose the normally open (NO) and normally closed (NC) switching strategy with a unified signal transmission mechanism to build molecular circuits. Specifically, the opposite logic can be output with or without a single signal, and the state of the switch can be adjusted by the addition order and time interval of the upstream signal and switch signal, which endows the switch with time-responsive characteristics. In addition, a time-delay relay with the function of delayed disconnection is developed to realize quantitative control of outputs, which has the potential to meet the automation control need of the system. Finally, digital square and square root circuits are constructed by cascading the NO and NC switches, which demonstrates the versatility of switches. Our design can be extended to time logic and complex digital computing circuits for use in information processing and nanomachines.

DNA-Based Molecular Machines

Artificial molecular machines have found widespread applications ranging from fundamental studies to biomedicine. More recent advances in exploiting unique physical and chemical properties of DNA have led to the development of DNA-based artificial molecular machines. The unprecedented programmability of DNA provides a powerful means to design complex and sophisticated DNA-based molecular machines that can exert mechanical force or motion to realize complex tasks in a controllable, modular fashion. This Perspective highlights the potential and strategies to construct artificial molecular machines using double-stranded DNA, functional nucleic acids, and DNA frameworks, which enable improved control over reaction pathways and motion behaviors. We also outline the challenges and opportunities of using DNA-based molecular machines for biophysics, biosensing, and biocomputing.

Operant conditioning of stochastic chemical reaction networks

Adapting one’s behavior to environmental conditions and past experience is a key trait of living systems. In the biological world, there is evidence for adaptive behaviors such as learning even in naturally occurring, non-neural, single-celled organisms. In the bioengineered world, advances in synthetic cell engineering and biorobotics have created the possibility of implementing lifelike systems engineered from the bottom up. This will require the development of programmable control circuitry for such biomimetic systems that is capable of realizing such non-trivial and adaptive behavior, including modification of subsequent behavior in response to environmental feedback. To this end, we report the design of novel stochastic chemical reaction networks capable of probabilistic decision-making in response to stimuli. We show that a simple chemical reaction network motif can be tuned to produce arbitrary decision probabilities when choosing between two or more responses to a stimulus signal. We further show that simple feedback mechanisms from the environment can modify these probabilities over time, enabling the system to adapt its behavior dynamically in response to positive or negative reinforcement based on its decisions. This system thus acts as a form of operant conditioning of the chemical circuit, in the sense that feedback provided based on decisions taken by the circuit form the basis of the learning process. Our work thus demonstrates that simple chemical systems can be used to implement lifelike behavior in engineered biomimetic systems.

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.

Kinetics of RNA and RNA:DNA Hybrid Strand Displacement

In nucleic acid nanotechnology, strand displacement is a widely used mechanism where one strand from a hybridized duplex is exchanged with an invading strand that binds to a toehold, a single-stranded region on the duplex. It is used to perform logic operations on a molecular level, initiate cascaded reactions, or even for in vivo diagnostics and treatments. While systematic experimental studies have been carried out to probe the kinetics of strand displacement in DNA with different toehold lengths, sequences, and mismatch positions, there has not been a comparable investigation of RNA or RNA-DNA hybrid systems. Here, we experimentally study how toehold length, toehold location (5' or 3' end of the strand), and mismatches influence the strand displacement kinetics. We observe reaction acceleration with increasing toehold length and placement of the toehold at the 5' end of the substrate. We find that mismatches closer to the interface of toehold and duplex slow down the reaction more than remote mismatches. A comparison of RNA and DNA displacement with hybrid displacement (RNA invading DNA or DNA invading RNA) is partly explainable by the thermodynamic stabilities of the respective toehold regions, but also suggests that the rearrangement from B-form to A-form helix in the case of RNA invading DNA might play a role in the kinetics.

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.

Proximity-induced exponential amplification reaction triggered by proteins and small molecules

We proposed a method to regulate nucleic acid polymerization by proximity and designed an ultrasensitive biosensor based on proximity-induced exponential amplification reaction for proximity assay of proteins (streptavidin) and small molecules (adenosine triphosphate), which allows us to detect a variety of interesting targets by simply changing the binding sites of DNA.

Programming DNA-Based Systems through Effective Molarity Enforced by Biomolecular Confinement

The fundamental concept of effective molarity is observed in a variety of biological processes, such as protein compartmentalization within organelles, membrane localization and signaling paths. To control molecular encountering and promote effective interactions, nature places biomolecules in specific sites inside the cell in order to generate a high, localized concentration different from the bulk concentration. Inspired by this mechanism, scientists have artificially recreated in the lab the same strategy to actuate and control artificial DNA-based functional systems. Here, it is discussed how harnessing effective molarity has led to the development of a number of proximity-induced strategies, with applications ranging from DNA-templated organic chemistry and catalysis, to biosensing and protein-supported DNA assembly.

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.

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.

Nonlinear Regulation of Enzyme-Free DNA Circuitry with Ultrasensitive Switches

DNA is used to construct synthetic chemical reaction networks (CRNs), such as inorganic oscillators and gene regulatory networks. Nonlinear regulation with a simpler molecular mechanism is particularly important in large-scale CRNs with complex dynamics, such as bistability, adaptation, and oscillation of cellular functions. Here we introduce a new approach based on ultrasensitive switches as modular regulatory elements to nonlinearly regulate DNA-based CRNs. The nonlinear behavior of the systems can be finely tuned by programmable regulation of the linker length and the ligand binding sites, of which the Hill coefficients (n_H) are in the range of 1.00-2.32. By integrating two different strand displacement reactions with low-order nonlinearities (n_H ≈ 1.44 and 1.54), we could construct CRNs exhibiting high-order nonlinearities with Hill coefficients of up to ∼2.70. In addition, this could provide an efficient approach for designing CRNs at will with complex chemical dynamics by incorporating our design with previously developed enzyme-free DNA circuits.

Nucleic Acid Databases and Molecular-Scale Computing

DNA outperforms most conventional storage media in terms of information retention time, physical density, and volumetric coding capacity. Advances in synthesis and sequencing technologies have enabled implementations of large synthetic DNA databases with impressive storage capacity and reliable data recovery. Several robust DNA storage architectures featuring random access, error correction, and content rewritability have been constructed with the potential for scalability and cost reduction. We survey these recent achievements and discuss alternative routes for overcoming the hurdles of engineering practical DNA storage systems. We also review recent exciting work on in vivo DNA memory including intracellular recorders constructed by programmable genome editing tools. Besides information storage, DNA could serve as a versatile molecular computing substrate. We highlight several state-of-the-art DNA computing techniques such as strand displacement, localized hybridization chain reactions, and enzymatic reaction networks. We summarize how these simple primitives have facilitated rational designs and implementations of in vitro DNA reaction networks that emulate digital/analog circuits, artificial neural networks, or nonlinear dynamic systems. We envision these modular primitives could be strategically adapted for sophisticated database operations and massively parallel computations on DNA databases. We also highlight in vivo DNA computing modules such as CRISPR logic gates for building scalable genetic circuits in living cells. To conclude, we discuss various implications and challenges of DNA-based storage and computing, and we particularly encourage innovative work on bridging these two areas of research to further explore molecular parallelism and near-data processing. Such integrated molecular systems could lead to far-reaching applications in biocomputing, security, and medicine.

Effective design principles for leakless strand displacement systems

Artificially designed molecular systems with programmable behaviors have become a valuable tool in chemistry, biology, material science, and medicine. Although information processing in biological regulatory pathways is remarkably robust to error, it remains a challenge to design molecular systems that are similarly robust. With functionality determined entirely by secondary structure of DNA, strand displacement has emerged as a uniquely versatile building block for cell-free biochemical networks. Here, we experimentally investigate a design principle to reduce undesired triggering in the absence of input (leak), a side reaction that critically reduces sensitivity and disrupts the behavior of strand displacement cascades. Inspired by error correction methods exploiting redundancy in electrical engineering, we ensure a higher-energy penalty to leak via logical redundancy. Our design strategy is, in principle, capable of reducing leak to arbitrarily low levels, and we experimentally test two levels of leak reduction for a core "translator" component that converts a signal of one sequence into that of another. We show that the leak was not measurable in the high-redundancy scheme, even for concentrations that are up to 100 times larger than typical. Beyond a single translator, we constructed a fast and low-leak translator cascade of nine strand displacement steps and a logic OR gate circuit consisting of 10 translators, showing that our design principle can be used to effectively reduce leak in more complex chemical systems.

Automated sequence-level analysis of kinetics and thermodynamics for domain-level DNA strand-displacement systems

As an engineering material, DNA is well suited for the construction of biochemical circuits and systems, because it is simple enough that its interactions can be rationally designed using Watson-Crick base pairing rules, yet the design space is remarkably rich. When designing DNA systems, this simplicity permits using functional sections of each strand, called domains, without considering particular nucleotide sequences. However, the actual sequences used may have interactions not predicted at the domain-level abstraction, and new rigorous analysis techniques are needed to determine the extent to which the chosen sequences conform to the system's domain-level description. We have developed a computational method for verifying sequence-level systems by identifying discrepancies between the domain-level and sequence-level behaviour. This method takes a DNA system, as specified using the domain-level tool Peppercorn, and analyses data from the stochastic sequence-level simulator Multistrand and sequence-level thermodynamic analysis tool NUPACK to estimate important aspects of the system, such as reaction rate constants and secondary structure formation. These techniques, implemented as the Python package KinDA, will allow researchers to predict the kinetic and thermodynamic behaviour of domain-level systems after sequence assignment, as well as to detect violations of the intended behaviour.

Improving the Performance of DNA Strand Displacement Circuits by Shadow Cancellation

DNA strand displacement circuits are powerful tools that can be rationally engineered to implement molecular computing tasks because they are programmable, cheap, robust, and predictable. A key feature of these circuits is the use of catalytic gates to amplify signal. Catalytic gates tend to leak; that is, they generate output signal even in the absence of intended input. Leaks are harmful to the performance and correct operation of DNA strand displacement circuits. Here, we present "shadow cancellation", a general-purpose technique to mitigate leak in catalytic DNA strand displacement circuits. Shadow cancellation involves constructing a parallel shadow circuit that mimics the primary circuit and has the same leak characteristics. It is situated in the same test tube as the primary circuit and produces "anti-background" DNA strands that cancel "background" DNA strands produced by leak. We demonstrate the feasibility and strength of the shadow leak cancellation approach through a challenging test case, a cross-catalytic feedback DNA amplifier circuit that leaks prodigiously. Shadow cancellation dramatically reduced the leak of this circuit and improved the signal-to-background difference by several fold. Unlike existing techniques, it makes no modifications to the underlying amplifier circuit and is agnostic to its leak mechanism. Shadow cancellation also showed good robustness to concentration errors in multiple scenarios. This work introduces a direction in leak reduction techniques for DNA strand displacement amplifier circuits and can potentially be extended to other molecular amplifiers.