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Biomolecular information processing
Edited by a renowned and much cited chemist, this book covers the whole span of molecular computers that are based on biomolecules. The contributions by all the major scientists in the field provide an excellent overview of the latest developments in this rapidly expanding area.
A must-have for all researchers working on this very hot topic.
Perfectly complements Molecular and Supramolecular Information Processing, also by Prof. Katz, and available as a two-volume set.
eBook
Implementing digital computing with DNA-based switching circuits
DNA strand displacement reactions (SDRs) provide a set of intelligent toolboxes for developing molecular computation. Whereas SDR-based logic gate circuits have achieved a high level of complexity, the scale-up for practical achievable computational tasks remains a hurdle. Switching circuits that were originally proposed by Shannon in 1938 and nowadays widely used in telecommunication represent an alternative and efficient means to realize fast-speed and high-bandwidth communication. Here we develop SDR-based DNA switching circuits (DSCs) for implementing digital computing. Using a routing strategy on a programmable DNA switch canvas, we show that arbitrary Boolean functions can be represented by DSCs and implemented with molecular switches with high computing speed. We further demonstrate the implementation of full-adder and square-rooting functions using DSCs, which only uses down to 1/4 DNA strands as compared with a dual-rail logic expression-based design. We expect that DSCs provide a design paradigm for digital computation with biomolecules.
DNA strand displacement reactions can be difficult to scale up for computational tasks. Here the authors develop DNA switching circuits that achieve high-speed computing with fewer molecules.
Journal Article
Integrating single-cell transcriptomic data across different conditions, technologies, and species
A new computational approach enables integrative analysis of disparate single-cell RNA–sequencing data sets by identifying shared patterns of variation between cell subpopulations.
Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (
http://satijalab.org/seurat/
), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.
Journal Article
Pathways to cellular supremacy in biocomputing
Synthetic biology uses living cells as the substrate for performing human-defined computations. Many current implementations of cellular computing are based on the “genetic circuit” metaphor, an approximation of the operation of silicon-based computers. Although this conceptual mapping has been relatively successful, we argue that it fundamentally limits the types of computation that may be engineered inside the cell, and fails to exploit the rich and diverse functionality available in natural living systems. We propose the notion of “cellular supremacy” to focus attention on domains in which biocomputing might offer superior performance over traditional computers. We consider potential pathways toward cellular supremacy, and suggest application areas in which it may be found.
Synthetic biology uses cells as its computing substrate, often based on the genetic circuit concept. In this Perspective, the authors argue that existing synthetic biology approaches based on classical models of computation limit the potential of biocomputing, and propose that living organisms have under-exploited capabilities.
Journal Article
DNA-based programmable gate arrays for general-purpose DNA computing
The past decades have witnessed the evolution of electronic and photonic integrated circuits, from application specific to programmable
1
,
2
. Although liquid-phase DNA circuitry holds the potential for massive parallelism in the encoding and execution of algorithms
3
,
4
, the development of general-purpose DNA integrated circuits (DICs) has yet to be explored. Here we demonstrate a DIC system by integration of multilayer DNA-based programmable gate arrays (DPGAs). We find that the use of generic single-stranded oligonucleotides as a uniform transmission signal can reliably integrate large-scale DICs with minimal leakage and high fidelity for general-purpose computing. Reconfiguration of a single DPGA with 24 addressable dual-rail gates can be programmed with wiring instructions to implement over 100 billion distinct circuits. Furthermore, to control the intrinsically random collision of molecules, we designed DNA origami registers to provide the directionality for asynchronous execution of cascaded DPGAs. We exemplify this by a quadratic equation-solving DIC assembled with three layers of cascade DPGAs comprising 30 logic gates with around 500 DNA strands. We further show that integration of a DPGA with an analog-to-digital converter can classify disease-related microRNAs. The ability to integrate large-scale DPGA networks without apparent signal attenuation marks a key step towards general-purpose DNA computing.
Generic single-stranded oligonucleotides used as a uniform transmission signal can reliably integrate large-scale DNA integrated circuits with minimal leakage and high fidelity for general-purpose computing.
Journal Article
DNA origami cryptography for secure communication
Biomolecular cryptography exploiting specific biomolecular interactions for data encryption represents a unique approach for information security. However, constructing protocols based on biomolecular reactions to guarantee confidentiality, integrity and availability (CIA) of information remains a challenge. Here we develop DNA origami cryptography (DOC) that exploits folding of a M13 viral scaffold into nanometer-scale self-assembled braille-like patterns for secure communication, which can create a key with a size of over 700 bits. The intrinsic nanoscale addressability of DNA origami additionally allows for protein binding-based steganography, which further protects message confidentiality in DOC. The integrity of a transmitted message can be ensured by establishing specific linkages between several DNA origamis carrying parts of the message. The versatility of DOC is further demonstrated by transmitting various data formats including text, musical notes and images, supporting its great potential for meeting the rapidly increasing CIA demands of next-generation cryptography.
Biomolecular cyptography that exploits specific interactions could be used for data encryption. Here the authors use the folding of M13 DNA to encrypt information for secure communication.
Journal Article
High-speed DNA-based rolling motors powered by RNase H
DNA-based machines that walk by converting chemical energy into controlled motion could be of use in applications such as next-generation sensors, drug-delivery platforms and biological computing. Despite their exquisite programmability, DNA-based walkers are challenging to work with because of their low fidelity and slow rates (∼1 nm min
–1
). Here we report DNA-based machines that roll rather than walk, and consequently have a maximum speed and processivity that is three orders of magnitude greater than the maximum for conventional DNA motors. The motors are made from DNA-coated spherical particles that hybridize to a surface modified with complementary RNA; the motion is achieved through the addition of RNase H, which selectively hydrolyses the hybridized RNA. The spherical motors can move in a self-avoiding manner, and anisotropic particles, such as dimerized or rod-shaped particles, can travel linearly without a track or external force. We also show that the motors can be used to detect single nucleotide polymorphism by measuring particle displacement using a smartphone camera.
A DNA-based rolling motor that is powered by RNA hydrolysis has a maximum speed and processivity that is three orders of magnitude greater than conventional DNA-based walkers.
Journal Article
Cancer diagnosis with DNA molecular computation
Early and precise cancer diagnosis substantially improves patient survival. Recent work has revealed that the levels of multiple microRNAs in serum are informative as biomarkers for the diagnosis of cancers. Here, we designed a DNA molecular computation platform for the analysis of miRNA profiles in clinical serum samples. A computational classifier is first trained in silico using miRNA profiles from The Cancer Genome Atlas. This is followed by a computationally powerful but simple molecular implementation scheme using DNA, as well as an effective in situ amplification and transformation method for miRNA enrichment in serum without perturbing the original variety and quantity information. We successfully achieved rapid and accurate cancer diagnosis using clinical serum samples from 22 healthy people (8) and people with lung cancer (14) with an accuracy of 86.4%. We envision that this DNA computational platform will inspire more clinical applications towards inexpensive, non-invasive and rapid disease screening, classification and progress monitoring.A DNA molecular computation platform allows the rapid diagnosis of lung cancer with high accuracy by analysing specific miRNA levels in clinical serum samples.
Journal Article
Scaling Up Digital Circuit Computation with DNA Strand Displacement Cascades
To construct sophisticated biochemical circuits from scratch, one needs to understand how simple the building blocks can be and how robustly such circuits can scale up. Using a simple DNA reaction mechanism based on a reversible strand displacement process, we experimentally demonstrated several digital logic circuits, culminating in a four-bit square-root circuit that comprises 130 DNA strands. These multilayer circuits include thresholding and catalysis within every logical operation to perform digital signal restoration, which enables fast and reliable function in large circuits with roughly constant switching time and linear signal propagation delays. The design naturally incorporates other crucial elements for large-scale circuitry, such as general debugging tools, parallel circuit preparation, and an abstraction hierarchy supported by an automated circuit compiler.
Journal Article