Оглавление
Группа авторов. DNA- and RNA-Based Computing Systems
Table of Contents
List of Tables
List of Illustrations
Guide
Pages
DNA- and RNA-Based Computing Systems
Preface
References
1 DNA Computing: Origination, Motivation, and Goals – Illustrated Introduction
1.1 Motivation and Applications
1.2 DNA‐ and RNA‐Based Biocomputing Systems in Progress
1.3 DNA‐Based Information Storage Systems
1.4 Short Conclusions and Comments on the Book
References
2 DNA Computing: Methodologies and Challenges
2.1 Introduction to DNA Computing Methodologies
2.2 Key Developments in DNA Computing
2.2.1 Adleman Model
2.2.2 Lipton's Model
2.2.3 Smith's Model
2.2.4 Sakamoto's Model
2.2.5 Ouyang's Model
2.2.6 Chao's Model
2.2.7 DNA Origami
2.2.8 DNA‐Based Data Storage
2.3 Challenges
Acknowledgment
References
3 DNA Computing and Circuits
3.1 From Theory to DNA Implementations
3.2 Application‐Specific DNA Circuits
Acknowledgments
References
4 Connecting DNA Logic Gates in Computational Circuits
4.1 DNA Logic Gates in the Context of Molecular Computation
4.2 Connecting Deoxyribozyme Logic Gates
4.3 Connecting Gates Based on DNA Strand Displacement
4.4 Logic Gates Connected Via DNA Four‐Way Junction (4WJ)
4.5 Conclusion
References
Note
5 Development of Logic Gate Nanodevices from Fluorogenic RNA Aptamers
5.1 Nucleic Acid: The Material of Choice for Nanotechnology
5.2 RNA Aptamers are Modular and Programmable Biosensing Units
5.3 Construction of RNA Nanoparticles with Integrated Logic Gate Operations Using Light‐Up Aptamers
5.3.1 Implementation of MG‐Binding RNA Aptamer to Design Binary Logic Gates
5.3.2 Implementation of MG‐Binding RNA Aptamer and Broccoli RNA Aptamer to Design Half‐Adder Circuit
5.4 Conclusion
Acknowledgments
References
6 Programming Molecular Circuitry and Intracellular Computing with Framework Nucleic Acids
6.1 Framework Nucleic Acids
6.2 A Toolbox for Biomolecular Engineering of Living Systems
6.2.1 Biomolecular Scaffolds
6.2.2 Logic Units
6.2.3 Cell Entry Vehicles
6.2.4 Isothermal Construction
6.2.5 Targeting and Editing
6.2.6 Signal Readout
6.2.7 Triggers and Switches
6.2.8 Error Correction and Resilience
6.3 Targeted Applications
6.3.1 Drug Delivery
6.3.2 Cellular Imaging
6.3.3 Metabolic Engineering and Cellular Pathway Investigation
6.4 Nucleic Acid Nanotechnology‐Enabled Computing Kernel
6.5 I/O and Human–Computer Interfacing
6.6 Information Storage
6.7 Perspectives
6.8 Conclusion
Terminology
References
7 Engineering DNA Switches for DNA Computing Applications
7.1 Introduction
7.2 Selecting Recognition Element Based on Input
7.3 Engineering Switching Mechanisms
7.4 Engineering Logic Output Function Response
7.5 Optimizing Switch Response
7.6 Perspective
Acknowledgments
References
8 Fluorescent Signal Design in DNA Logic Circuits
8.1 Basic Signal Generation Strategies Based on DNA Structures
8.1.1 Strategies Based on Watson–Crick Hydrogen Bond. 8.1.1.1 Signal Derived from Hairpin Structure/Molecular Beacon
8.1.1.2 Signal Derived from DNAzyme Activity
8.1.1.3 Signal Derived from Strand Displacement Reaction
8.1.2 Strategies Based on Hoogsteen Hydrogen Bond
8.1.2.1 Signal Derived from G‐Quadruplex
8.1.2.2 Signal with the Help of i‐Motif
8.1.3 Signal Derived from Aptamer–Ligand Interaction
8.2 Designs for Constructing Multi‐output Signals
8.2.1 Selecting Individual Signal Transducers
8.2.2 Designing Multifunctional Probes
8.3 Summary and Outlook
References
9 Nontraditional Luminescent and Quenching Materials for Nucleic Acid‐Based Molecular Photonic Logic
9.1 Introduction
9.2 DNA Molecular Photonic Logic Gates
9.3 Nontraditional Luminescent Materials
9.4 Semiconductor “Quantum Dot” Nanocrystals. 9.4.1 Quantum Dots
9.4.2 Logic Gates with QDs
9.5 Lanthanide‐Based Materials. 9.5.1 Luminescent Lanthanide Complexes
9.5.2 Coupling Lanthanide Complexes with Energy Transfer
9.5.3 Logic Gates with LLCs and Lanthanide Ions
9.5.4 Upconversion Nanoparticles
9.5.5 Logic Gates with UCNPs
9.6 Gold Nanoparticles. 9.6.1 Gold Nanoparticles
9.6.2 Logic Gates with AuNPs and Colorimetric Output
9.6.3 Logic Gates with AuNPs and PL Quenching
9.7 Metal Nanoclusters. 9.7.1 Metal Nanoclusters
9.7.2 Logic Gates with Metal Nanoclusters
9.8 Carbon Nanomaterials
9.8.1 Graphene and Graphene Oxide
9.8.2 Logic Gates with Graphene and GO
9.8.3 Carbon Dots
9.8.4 Logic Gates with CDs
9.9 Conjugated Polymers. 9.9.1 Conjugated Polymers
9.9.2 Logic Gates with CPs
9.10 Conclusions and Perspective
References
10 Programming Spatiotemporal Patterns with DNA‐Based Circuits
10.1 Introduction. 10.1.1 What is Spatial Computing?
10.1.2 Digital vs. Analog Computing
10.1.3 Computing Consumes Energy
10.1.4 Molecules Compute in Space Through Reaction–Diffusion Primitives
10.2 Experimental Implementation of DNA Analog Circuits
10.2.1 DNA Strand Displacement Oscillators
10.2.2 DNA/Enzyme Oscillators
10.2.2.1 Genelets
10.2.2.2 PEN Reactions
10.3 Time‐Dependent Spatial Patterns
10.3.1 Edge Detection
10.3.2 Traveling Patterns
10.3.2.1 Fronts
10.3.2.2 Go‐Fetch Fronts
10.3.2.3 Waves and Spirals
10.3.3 Controlling Spatio‐Temporal Patterns
10.3.3.1 Controlling Diffusion Coefficients
10.3.3.2 Initial and Boundary Conditions
10.4 Steady‐State Spatial Patterns
10.4.1 Colony Formation
10.4.2 Patterns with Positional Information
10.5 Conclusion and Perspectives
Acknowledgments
References
11 Computing Without Computing: DNA Version
11.1 Introduction
11.2 Computing Without Computing – Quantum Version: A Brief Reminder
11.3 Computing Without Computing – Version Involving Acausal Processes: A Reminder
11.4 Computing Without Computing: – DNA Version. 11.4.1 Main Idea
11.4.2 It Is Not Easy to Stop Biological Processes
11.4.3 Towards Describing Ligation Prevention in Precise Terms
11.4.4 What Is Given
11.4.5 What We Want to Find
11.4.6 Let Us Prove that the Ligation Prevention Problem Is NP‐Hard
11.4.7 How NP‐Hardness Is Usually Proved
11.4.8 How We Will Prove NP‐Hardness
11.4.9 The Actual Proof by Reduction
11.5 DNA Computing Without Computing Is Somewhat Less Powerful than Traditional DNA Computing: A Proof. 11.5.1 Which of the Two DNA Computing Schemes is More Powerful?
11.5.2 W‐hierarchy: A Brief Reminder
11.5.3 Conclusion
11.6 First Related Result: Security Is More Difficult to Achieve than Privacy. 11.6.1 What We Plan to do in this Section
11.6.2 How to Describe Privacy in Graph Terms
11.6.3 How to Describe Security in Graph Terms
11.6.4 Conclusion: Security Is More Difficult to Maintain than Privacy
11.7 Second Related Result: Data Storage Is More Difficult than Data Transmission. 11.7.1 Application to Information Science
11.7.2 Data Storage
11.7.3 Data Transmission
11.7.4 Conclusion: Data Storage Is More Difficult than Data Transmission
Acknowledgments
References
12 DNA Computing: Versatile Logic Circuits and Innovative Bio‐applications
12.1 Definition, Logical Principle, and Classification of DNA Computing
12.2 Advanced Arithmetic DNA Logic Devices. 12.2.1 Half‐Adder, Half‐Subtractor
12.2.2 Full‐Adder, Full‐Subtractor
12.3 Advanced Non‐arithmetic DNA Logic Devices
12.3.1 Data Conversion: Encoder/Decoder, Multiplexer/Demultiplexer
12.3.2 Distinguishing Even/Odd Natural Numbers: The Parity Checker
12.3.3 DNA Voter and Keypad Lock
12.3.4 Parity Generator/Checker (pG/pC) for Error Detection During Data Transmission
12.3.5 Non‐Boolean Ternary Logic Gates
12.4 Concatenated Logic Circuits
12.5 Innovative Multifunctional DNA Logic Library
12.6 Intelligent Bio‐applications
12.7 Prospects
Acknowledgment
References
13 Nucleic Acid‐Based Computing in Living Cells Using Strand Displacement Processes
13.1 Nucleic Acid Strand Displacement
13.1.1 Basics
13.1.2 Computing with Strand Displacement Processes
13.1.3 Computing with Nucleic Acid Strand Displacement In Vivo
13.2 Synthetic Riboregulators. 13.2.1 First‐Generation Riboregulators
13.2.2 Toehold Switch Riboregulators
13.2.3 Other Transcriptional and Translational Regulators
13.3 Combining Strand Displacement and CRISPR Mechanisms. 13.3.1 A Brief Introduction to CRISPR
13.4 Computing Via Nucleic Acid Strand Displacement in Mammalian Cells
13.5 Outlook. 13.5.1 Interfacing Nucleic Acid Computing with Synthetic Biology
References
14 Strand Displacement in DNA‐Based Nanodevices and Logic
14.1 An Introduction to Strand Displacement Reactions
14.1.1 External Control of Strand Displacement Reactions
14.1.2 The Toehold Exchange Mechanism
14.2 Dynamic Reconfiguration of Structural Devices
14.3 Stepped and Autonomous DNA Walkers
14.4 Early Breakthroughs in DNA Computing
14.4.1 Hamiltonian Paths
14.4.2 Satisfiability (SAT) Problem
14.5 DNA‐Based Molecular Logic. 14.5.1 Computing with Boolean Logic
14.5.2 Deoxyribozyme Logic Gates
14.5.3 Autonomous DNA Translators
14.5.4 Catalytic Systems for Signal Amplification
14.6 Future Prospects for Strand Displacement‐Based Devices. 14.6.1 DNA Chemical Reaction Networks
14.6.2 DNA Nanotechnology Goes In Vivo
Acknowledgment
References
15 Development and Application of Catalytic DNA in Nanoscale Robotics
15.1 Introduction
15.2 Brief History of DNAzymes
15.3 Experimental Implementations
15.4 DNAzyme Walkers
15.5 Statistical Mechanics and Simulation
15.6 Conclusions
References
16 DNA Origami Transformers
16.1 Introduction
16.2 Design
16.3 Experimental Demonstrations
16.4 Applications
16.5 Conclusion
Acknowledgment
References
17 Nanopore Decoding for DNA Computing
Abbreviations
17.1 Introduction
17.2 Application of Nanopore Technology for Rapid and Label‐Free Decoding
17.3 Application of Nanopore Decoding in Medical Diagnosis
17.4 Conclusions
References
18 An Overview of DNA‐Based Digital Data Storage
18.1 Introduction
18.1.1 Durability and Energy Efficiency
18.1.2 Density and Coding Capacity
18.1.3 Availability of Supporting Technologies
18.2 Components of a DNA Storage System
18.2.1 Data Encoding
18.2.2 Data Writing
18.2.3 Data Storage
18.2.4 Data Retrieval
18.2.5 Data Decoding
18.3 Conclusions and Outlook
Acknowledgments
References
19 Interfacing Enzyme‐Based and DNA‐Based Computing Systems: From Simple Boolean Logic to Sophisticated Reversible Logic Systems
19.1 Interfacing Enzyme‐Based and DNA‐Based Computing Systems is a Challenging Goal: Motivations and Approaches
19.2 Bioelectronic Interface Transducing Logically Processed Signals from an Enzymatic System to a DNA System
19.3 The Bioelectronic Interface Connecting Enzyme‐Based Reversible Logic Gates and DNA‐Based Reversible Logic Gates: Realization in a Flow Device
19.4 Enzyme‐Based Fredkin Gate Processing Biomolecular Signals Prior to the Bioelectronic Interface
19.5 Reversible DNA‐Based Feynman Gate Activated by Signals Produced by the Enzyme‐Based Fredkin Gate
19.6 Conclusions and Perspectives
19.A Appendix. 19.A.1 Oligonucleotides Used in the System Mimicking Feynman Gate
References
20 Conclusions and Perspectives: Further Research Directions and Possible Applications
Index
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