Parallel molecular computation on digital data stored in DNA

Random Sanitization in DNA Information Storage Using CRISPR-Cas12a

DNA information storage provides an excellent solution for metadata storage due to its high density, programmability, and long-term stability. However, current research primarily focuses on the processes of storing and reading data, lacking comprehensive solutions for secure metadata wiping. Herein, we present a method of random sanitization in DNA information storage using CRISPR-Cas12a (RSDISC) based on precise control of the thermodynamic energy of primer-template hybridization. We utilize the collateral cleavage (trans-activity) of single-stranded DNA (ssDNA) by CRISPR-Cas12a to achieve selective sanitization of files in metadata. This method enables ssDNA degradation with different GC contents, lengths, and secondary structures to achieve a sanitization efficiency up to 99.9% for 28,258 oligonucleotides in DNA storage within one round. We demonstrate that the number of erasable files could reach 1012 based on a model of primer-template hybridization efficiency. Overall, RSDISC provides a random sanitization approach to set the foundation of information encryption, file classification, memory deallocation, and accurate reading in DNA storage.

Instruction-responsive programmable assemblies with DNA origami block pieces

DNA nanotechnology has created a wide variety of nanostructures that provide a reliable platform for nanofabrication and DNA computing. However, constructing programmable finite arrays that allow for easy pre-functionalization remains challenge. We aim to create more standardized and controllable DNA origami components, which could be assembled into finite-scale and more diverse superstructures driven by instruction sets. In this work, we designed and implemented DNA origami building block pieces (DOBPs) with eight mutually independent programmable edges and formulated DNA instructions that tailored such components. This system enables DOBPs to be assembled into one or more specific 2D arrays according to the instruction sets. Theoretically, a two-unit system can generate up to 48 distinct DNA arrays. Importantly, experiments results demonstrated that DOBPs are capable of both deterministic and nondeterministic assemblies. Moreover, after examining the effects of different connection strategies and instruction implementations on the yield of the target structures, we assembled more complex 2D arrays, including limited self-assembly arrays such as 'square frames', 'windmills' and 'multiples of 3' long strips. We also demonstrated examples of Boolean logic gates 'AND' and 'XOR' computations based on these assembly arrays. The assembly system provides a model nano-structure for the research on controllable finite self-assembly and offers a more integrated approach for the storage and processing of molecular information.

Empowering DNA-Based Information Processing: Computation and Data Storage

Information processing is a critical topic in the digital age, as silicon-based circuits face unprecedented challenges such as data explosion, immense energy consumption, and approaching physical limits. Deoxyribonucleic acid (DNA), naturally selected as a carrier for storing and using genetic information, possesses unique advantages for information processing, which has given rise to the emerging fields of DNA computing and DNA data storage. To meet the growing practical demands, a wide variety of materials and interfaces have been introduced into DNA information processing technologies, leading to significant advancements. This review summarizes the advances in materials and interfaces that facilitate DNA computation and DNA data storage. We begin with a brief overview of the fundamental functions and principles of DNA computation and DNA data storage. Subsequently, we delve into DNA computing systems based on various materials and interfaces, including microbeads, nanomaterials, DNA nanostructures, hydrophilic-hydrophobic compartmentalization, hydrogels, metal-organic frameworks, and microfluidics. We also explore DNA data storage systems, encompassing encapsulation materials, microfluidics techniques, DNA nanostructures, and living cells. Finally, we discuss the current bottlenecks and obstacles in the fields and provide insights into potential future developments.

Advancements in DNA computing: exploring DNA logic systems and their biomedical applications

DNA computing is regarded as one of the most promising candidates for the next generation of molecular computers, utilizing DNA to execute Boolean logic operations. In recent decades, DNA computing has garnered widespread attention due to its powerful programmable and parallel computing capabilities, demonstrating significant potential in intelligent biological analysis. This review summarizes the latest advancements in DNA logic systems and their biomedical applications. Firstly, it introduces recent DNA logic systems based on various materials such as functional DNA sequences, nanomaterials, and three-dimensional DNA nanostructures. The material innovations driving DNA computing have been summarized, highlighting novel molecular reactions and analytical performance metrics like efficiency, sensitivity, and selectivity. Subsequently, it outlines the biomedical applications of DNA computing-based multi-biomarker analysis in cellular imaging, clinical diagnosis, and disease treatment. Additionally, it discusses the existing challenges and future research directions for the development of DNA computing.

Advanced DNA Amplification for Efficient Data Storage

DNA amplification technologies have significantly advanced biotechnology, particularly in DNA storage. However, adaptation of these technologies to DNA storage poses substantial challenges. Key bottlenecks include achieving high throughput to manage large data sets, ensuring rapid and efficient DNA amplification, and minimizing bias to maintain data fidelity. This perspective begins with an overview of natural and artificial amplification strategies, such as polymerase chain reaction and isothermal amplification, highlighting their respective advantages and limitations. It then explores the prospective applications of these techniques in DNA storage, emphasizing the need to optimize protocols for scalability and robustness in handling diverse digital data. Concurrently, we identify promising avenues, including advancements in enzymatic processes and novel amplification methodologies, poised to mitigate existing constraints and propel the field forward. Ultimately, we provide insights into how to utilize advanced DNA amplification strategies poised to revolutionize the efficiency and feasibility of data storage, ushering in enhanced approaches to data retrieval in the digital age.

SemiSynBio: A new era for neuromorphic computing

Neuromorphic computing has the potential to achieve the requirements of the next-generation artificial intelligence (AI) systems, due to its advantages of adaptive learning and parallel computing. Meanwhile, biocomputing has seen ongoing development with the rise of synthetic biology, becoming the driving force for new generation semiconductor synthetic biology (SemiSynBio) technologies. DNA-based biomolecules could potentially perform the functions of Boolean operators as logic gates and be used to construct artificial neural networks (ANNs), providing the possibility of executing neuromorphic computing at the molecular level. Herein, we briefly outline the principles of neuromorphic computing, describe the advances in DNA computing with a focus on synthetic neuromorphic computing, and summarize the major challenges and prospects for synthetic neuromorphic computing. We believe that constructing such synthetic neuromorphic circuits will be an important step toward realizing neuromorphic computing, which would be of widespread use in biocomputing, DNA storage, information security, and national defense.

Shotgun Sequencing of 512-mer Copolyester Allows Random Access to Stored Information

Digital information encoded in polymers has been exclusively decoded by mass spectrometry. However, the size limit of analytes in mass spectrometry restricts the storage capacity per chain. In addition, sequential decoding hinders random access to the bits of interest without full-chain sequencing. Here we report the shotgun sequencing of a 512-mer sequence-defined polymer whose molecular weight (57.3 kDa) far exceeds the analytical limit of mass spectrometry. A 4-bit fragmentation code was implemented at aperiodic positions during the synthetic encoding of 512-bit information without affecting storage capacity per chain. Upon activating the fragmentation code, the polymer chain splits into 18 oligomers, which could be individually decoded by tandem-mass sequencing. These sequences were computationally reconstructed into a full sequence using an error-detection method. The proposed sequencing method eliminates the storage limit of a single polymer chain and allows random access to the bits of interest without full-chain sequencing.

DNA Molecular Computation Using the CRISPR-Mediated Reaction and Surface Growth of Gold Nanoparticles

DNA has emerged as a promising tool to build logic gates for biocomputing. However, prevailing methodologies predominantly rely on hybridization reactions or structural alterations to construct DNA logic gates, which are limited in simplicity and diversity. Herein, we developed simple and smart DNA-based logic gates for biocomputing through the DNA-mediated growth of gold nanomaterials without precise structure design and probe modification. Capitalizing on their excellent plasmonic properties, the surface growth of gold nanomaterials enables distinct wavelength shifts and unique shapes, which are modulated by the composition, length, and concentration of the DNA sequences. Combined with a CRISPR-mediated reaction, we constructed DNA circuits to achieve complicated biocomputing to modulate the surface growth of gold nanomaterials. By implementing logic functions controlled by input-mediated growth of gold nanomaterials, we established YES/NOT, AND/NAND, OR/NOR, XOR, and INHIBIT gates and further constructed cascade logic circuits, parity checker for natural numbers, and gray code encoder, which are promising for DNA biocomputing.

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.

Data Storage Using DNA

The exponential growth of global data has outpaced the storage capacities of current technologies, necessitating innovative storage strategies. DNA, as a natural medium for preserving genetic information, has emerged as a highly promising candidate for next-generation storage medium. Storing data in DNA offers several advantages, including ultrahigh physical density and exceptional durability. Facilitated by significant advancements in various technologies, such as DNA synthesis, DNA sequencing, and DNA nanotechnology, remarkable progress has been made in the field of DNA data storage over the past decade. However, several challenges still need to be addressed to realize practical applications of DNA data storage. In this review, the processes and strategies of in vitro DNA data storage are first introduced, highlighting recent advancements. Next, a brief overview of in vivo DNA data storage is provided, with a focus on the various writing strategies developed to date. At last, the challenges encountered in each step of DNA data storage are summarized and promising techniques are discussed that hold great promise in overcoming these obstacles.

Rapid Information Retrieval from DNA Storage with Microfluidic Very Large-Scale Integration Platform

Due to its high information density, DNA is very attractive as a data storage system. However, a major obstacle is the high cost and long turnaround time for retrieving DNA data with next-generation sequencing. Herein, the use of a microfluidic very large-scale integration (mVLSI) platform is described to perform highly parallel and rapid readout of data stored in DNA. Additionally, it is demonstrated that multi-state data encoded in DNA can be deciphered with on-chip melt-curve analysis, thereby further increasing the data content that can be analyzed. The pairing of mVLSI network architecture with exquisitely specific DNA recognition gives rise to a scalable platform for rapid DNA data reading.