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Open Access 2025 | Open Access | Buch

Biological Computing

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This open access book comprehensively introduces biocomputing, focusing on its foundational theories, experimental operations, and computational models. Biocomputing represents an innovative computational paradigm that leverages biomolecules as a carrier for processing and storing information. As a core enabler of human progress, computational tools serve as critical benchmarks of societal advancement and are key drivers of technological innovation. While electronic computers currently dominate as the primary instruments for information processing, their underlying manufacturing technologies are approaching physical and practical limits. This has prompted the search for alternative computational models and tools to meet the demands of future advancements.

Over recent decades, scientists have explored various fields to develop novel computational frameworks. These efforts have led to the emergence of groundbreaking paradigms, such as artificial neural networks inspired by brain-like information processing, evolutionary computing based on genetic mechanisms, biocomputing utilizing the unique properties of biomolecules, quantum computing exploiting quantum phenomena, and optical computing leveraging the properties of light.

Designed as an essential resource for readers interested in the principles and applications of biocomputing, this book establishes a foundational understanding of the subject while serving as a bridge to more advanced theoretical and practical exploration. A basic knowledge of molecular biology is recommended for readers to engage with the material fully.

The translation was done using artificial intelligence. Subsequently, a human revision was done primarily in terms of content.

Inhaltsverzeichnis

Frontmatter

Open Access

Chapter 1. Introduction
Abstract
Biological computation refers to the computation that uses biological macromolecules as data for information processing. Biological macromolecules mainly include DNA, RNA, and proteins, and consequently, biological computation can be divided into DNA computation, RNA computation, and protein computation. Limited by the level of biochemical operation technology, biological computation research is currently mainly focused on DNA computation. This book focuses on DNA computation and also gives a certain introduction to RNA computation and protein computation. This chapter introduces the background of the emergence of biological computation, research significance, and research progress.
Jin Xu

Open Access

Chapter 2. Graphs and Computational Complexity
Abstract
Chapter 1 pointed out that NP-complete problems are a “stumbling block” hindering the development of today’s technology. Due to the natural advantage of DNA computing’s parallelism in solving NP-complete problems, research on DNA computing over the past decades has mainly focused on solving NP-complete problems. Considering that many NP-complete problems are graph theory problems, this chapter first introduces some basic knowledge in graph theory; then, it gradually reveals the true nature of NP-complete problems and introduces some related theories of NP-complete problems, especially computational complexity theory.
Jin Xu

Open Access

Chapter 3. Biological Computing: Data
Abstract
As known from Chap. 1, biological computing is divided into DNA computing, RNA computing, and protein computing. The data used are DNA, RNA, and proteins, respectively. This chapter mainly introduces the physical and chemical characteristics and computational characteristics of these three types of biological macromolecules, which are the basis of biological computing.
Jin Xu

Open Access

Chapter 4. Biological Computing Operators: Enzymes and Biochemical Operations
Abstract
The previous chapter introduced the data required for biological computing: DNA, RNA, and proteins. They form the basis of biological computing. This chapter presents another important foundation of biological computing—basic operators: biological enzymes and biochemical operations.
Jin Xu

Open Access

Chapter 5. DNA Coding Theory and Algorithms
Abstract
DNA computing is an emerging computational model that has garnered significant attention due to its distinctive advantages at the molecular biological level. Since it was introduced by Adelman in 1994, this field has made remarkable progress in solving NP-complete problems, enhancing information security, encrypting images, controlling diseases, and advancing nanotechnology. A key challenge in DNA computing is the design of DNA coding, which aims to minimize nonspecific hybridization and enhance computational reliability. The DNA coding design is a classical combinatorial optimization problem focused on generating high-quality DNA sequences that meet specific constraints, including distance, thermodynamics, secondary structure, and sequence requirements. This chapter comprehensively examines the advancements in DNA coding design, highlighting mathematical models, counting theory, and commonly used DNA coding methods.
Jin Xu

Open Access

Chapter 6. Enumerative DNA Computing Model
Abstract
From 1994 to 2004, research in DNA computing was in its nascent stage, encompassing various aspects such as computational models, encoding, experimentation, and detection. Notably, during this period, the computational models were primarily enumerative in nature. These pioneering research outcomes not only laid the foundation for the deeper exploration of DNA computing but also provided a solid groundwork for RNA computing and, more broadly, the entire field of biological computing. This chapter focuses on the enumerative DNA computing models, selecting a subset of representative achievements for detailed introduction and in-depth analysis.
Jin Xu

Open Access

Chapter 7. Non-enumerative DNA Computation Model for Graph Vertex Coloring
Abstract
The previous chapter introduced the enumerative DNA computation model for solving NP-complete problems, but with the increase of problem size, the amount of DNA molecules in the generated initial solution space will inevitably show an “exponential explosion”.
Jin Xu

Open Access

Chapter 8. Parallel Vertex Coloring DNA Computing Model
Abstract
The non-enumerative DNA computing model presented in the preceding chapter has the remarkable ability to eliminate a vast number of non-solutions during the construction of the solution space. This effectively surmounts the issue of the exponential explosion of the solution space, thereby laying a solid foundation for further exploration into employing DNA molecules to address larger-scale and more complex problems.
Jin Xu

Open Access

Chapter 9. Probe Machine
Abstract
This chapter introduces an underlying fully parallel computing model—the probe machine. Unlike traditional Turing machines, the probe machine overcomes the limitations of sequential computation by enabling any two data to process information simultaneously without relying on linear adjacency. This fully parallel computing mechanism makes the probe machine highly efficient in solving complex problems, typically requiring only one or a few probe operations to obtain all solutions.
Jin Xu

Open Access

Chapter 10. DNA Algorithmic Self-Assembly
Abstract
Since Seeman proposed using DNA as a nanomaterial, DNA Tile and DNA origami technologies have been successively proposed, and DNA has realized the programmable assembly from two-dimensional patterns to three-dimensional spatial structures. The nanostructures constructed by DNA have stable properties, regular geometric appearance, and spatial addressability, and are widely used in many fields. DNA algorithmic self-assembly can be realized by controlling the self-assembly process in ways such as sequence and connection rules. With the programmability of DNA sequences, the design of DNA nanostructures to realize DNA algorithmic self-assembly has made good progress. This chapter mainly includes DNA Tile computation, Turing equivalent Tile computation, programmable Tile structure, single-strand Tile (SST) computation, universal DNA Tile computation, DNA origami computation, etc.
Jin Xu

Open Access

Chapter 11. RNA Computing
Abstract
The emergence of DNA computing models naturally gave rise to the development of RNA computing. However, over the past three decades, DNA computing has advanced rapidly, while RNA computing has progressed more slowly, primarily due to the structural characteristics of RNA molecules. This chapter focuses on the computational properties of RNA molecules, RNA computing models for solving NP-complete problems, and related research on RNA computing in the context of logic gates and logic circuits.
Jin Xu

Open Access

Chapter 12. Protein Computing
Abstract
Feynman’s vision of “developing computers at the molecular scale” led to the birth of the DNA computing model in 1994, followed by protein computing in 1995: a protein computing model of 2-state logic gates was proposed. Since then, many scholars have studied numerous protein logic gates, logic calculators, arithmetic calculators, protein computing models for solving NP-complete problems, protein storage and computing devices, etc. This chapter introduces some typical representatives.
Jin Xu
Metadaten
Titel
Biological Computing
verfasst von
Jin Xu
Copyright-Jahr
2025
Verlag
Springer Nature Singapore
Electronic ISBN
978-981-9638-70-3
Print ISBN
978-981-9638-69-7
DOI
https://doi.org/10.1007/978-981-96-3870-3